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Alayon, M. Gonzalez de la Rosa, F.J. Fumero, J.F. Sigut Saavedra, J.L. Sanchez" "autores" => array:5 [ 0 => array:4 [ "nombre" => "S." "apellidos" => "Alayon" "email" => array:1 [ 0 => "silvia@isaatc.ull.es" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">¿</span>" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "M." "apellidos" => "Gonzalez de la Rosa" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 2 => array:3 [ "nombre" => "F.J." "apellidos" => "Fumero" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 3 => array:3 [ "nombre" => "J.F." 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(A) Reference point and directional axes 1–8; (B) measuring distances and (C) pattern: mean radii AR1–AR8 and reference point.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Glaucoma is the most frequent cause of irreversible blindness in the population of industrialized countries. The best-known methods for diagnosing and controlling the evolution of glaucoma are optic nerve head (ONH) morphology assessment, establishing the thickness of the retinal optic nerve fiber layer and the subjective analysis of retinal sensitivity in the visual field of the patient.</p><p id="par0010" class="elsevierStylePara elsevierViewall">The inter-patient variability of the ONH appearance is very high. Considerable experience is required not only to differentiate a normal ONH from a diseased one but also to precisely define its anatomic limits. The methods for analyzing the surface morphology of the papilla by means of direct observation, such as confocal scanning laser (HRT) or stereoscopic images, to not allow a view of the internal structure of the ONH and therefore subjective operator assessments are applied. However, observation by means of optic coherence tomography (OCT) is able to define the internal edges of the scleral channel.</p><p id="par0015" class="elsevierStylePara elsevierViewall">It would be desirable to establish criteria to develop a closer match between the results of subjective procedures and the objective OCT measurements or, in the best of cases, to perform an automatic definition of the papillary contour.<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1–4</span></a> Precise ONH segmentations are essential to achieve this purpose and therefore many research papers have focused on this issue from the medical viewpoint<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5–7</span></a> as well as from that of software developers.<a class="elsevierStyleCrossRefs" href="#bib0040"><span class="elsevierStyleSup">8–10</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">Interobserver variability cannot be entirely removed although the identification of its causes and a quantitative assessment could make a decisive contribution for improving the quality of clinical practice and research.<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a> The study of this type of interobserver variability in clinical practice has been addressed in numerous medical activities such as cardiology,<a class="elsevierStyleCrossRefs" href="#bib0060"><span class="elsevierStyleSup">12,13</span></a> radiology,<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a> neonatology<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> or in the domain of ophthalmology.<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5,16–18</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">The interest in achieving precise and reproducible definitions of ONH limits has prompted the authors to measure interobserver variability and the patents thereof against the objective measurements provided by OCT. In parallel and also as a consequence and extension of this work, we have aimed at designing a computer tool, which was given the name of RIM-ONE, for self-assessment and training in manual definition of papillary edges. This has been developed as part of a joint ONH analysis research project by 3 Spanish hospitals, i.e. the Canarias University Hospital, the San Carlos Clinic Hospital and the Miguel Servet University Hospital.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Subjects, materials and methods</span><p id="par0030" class="elsevierStylePara elsevierViewall">This study utilized 169 ONH images (118 healthy volunteers, 11 ocular hypertensive patients, 12 incipient glaucomas, 14 moderate and 14 advanced glaucomas) taken by means of various non-midriatic retinographs at the 3 above mentioned hospitals, thus guaranteeing a representative and heterogeneous sampling. All the retinographs were captured with specific flash intensities to avoid color saturation.</p><p id="par0035" class="elsevierStylePara elsevierViewall">The images were collected in accordance with the ethical standards established in the 1964 Helsinki declaration. Ethical committee approval was obtained and the patients were informed about the objectives of this study. The utilized images are available in the RIM-ONE website<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> although said database does not provide detailed clinical information about the photographed subjects.</p><p id="par0040" class="elsevierStylePara elsevierViewall">The reference taken (gold standard) was the middle position of the manual limits of the papillary edge performed by 5 experts. To this end, the mean centroid of all the contours was calculated and taken as a reference point (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>A). On the basis of this point, 8 axes were drawn (at 45° intervals) measuring the <span class="elsevierStyleItalic">average radius</span> (AR) or the mean distance to the intersection of each of the 5 contours (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>B, d1–d5 and C, AR1–AR8).</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0045" class="elsevierStylePara elsevierViewall">Subsequently, the deviation of each expert against the pattern was compared as a measure of inter-observer variability. For each definition, the variability percentage against the patterns in each direction (<span class="elsevierStyleItalic">i</span>) was calculated by means of the variation coefficient (<span class="elsevierStyleItalic">VC</span>), i.e.:<elsevierMultimedia ident="eq0005"></elsevierMultimedia></p><p id="par0050" class="elsevierStylePara elsevierViewall">The RIM-ONE website includes the Matlab programming language code (Mathworks, Massachusetts, USA)<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a> which automatically calculates said measurements, comparing the results of each manual ONH definition with the pattern contour of the image.</p><p id="par0055" class="elsevierStylePara elsevierViewall">After obtaining the deviations of each expert against the pattern, interobserver variability was studied comparing deviations with each other (mean and standard deviation [SD]).</p><p id="par0060" class="elsevierStylePara elsevierViewall">In the second part of our study, the areas estimated on the basis of the number of pixels inside the contour defined by each expert were compared in 26 cases with the appropriate measures provided by OCT-Cirrus.</p><p id="par0065" class="elsevierStylePara elsevierViewall">The web training and self-assessment tool included in the RIM-ONE<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> website is in Java language, making it compatible with the most popular operative systems. It can be downloaded for free and it has been designed to be used also as an assessment tool for automatic segmentation algorithm designers. RIM-ONE allows free downloads of a number of retinographies (of healthy eyes and of eyes with various degrees of glaucoma development), and includes patterns for each image as well as proposing a common methodology for comparing the results of each segmentation against the pattern.</p><p id="par0070" class="elsevierStylePara elsevierViewall">The operation of the software application is very simple. Briefly, it does the following: (a) an approximate definition of the optic nerve head by means of an ellipse (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>); (b) precise definition of the ONH edge in predetermined radii (<a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>); (c) automatic calculation of the variability between user segmentation and the pattern, and (d) presentation of results.</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><elsevierMultimedia ident="fig0015"></elsevierMultimedia></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Results</span><p id="par0075" class="elsevierStylePara elsevierViewall">The mean variability results between each expert and the pattern for all images and redirections are shown in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>. Positive or negative values indicate differences against the pattern in one direction or another, i.e. the radius marked by the user is smaller or larger than the mean radius. The differences in the size of the chosen radius against the mean value are of 5.2%, without significant differences between sectors. There are significant mean differences between each expert and the other ones (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05), except between the second and the third, as well as between all the cases in some of the radii (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001).</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0080" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a> illustrates the mean and variability of each radius between all the experts (for each direction) in absolute value. In this context, the mean value is the value of the mean differences of the radii marked by the users with the mean radius (pattern). The source from which the differences are measured is located at the end of the pattern radius (mean radius) and not in the center of the papilla. The SD values clearly show that the adjustments made by each individual against the mean value is not uniform in all radii, which indicates a high degree of subjectivity in the form of carrying out the definition. Variability was lower in upper sectors (R1, R2 and R8), with significant differences against lower sectors (R4, R5 and R6, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.005) and of these vis-à-vis horizontal sectors (nasal R3 and temporal R7, <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.005) which exhibited the highest variability.</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0085" class="elsevierStylePara elsevierViewall">The variation coefficient of the subjective measurement of the papillary area was of 9.8%. For OCT, the mean papillary size was of 1.79<span class="elsevierStyleHsp" style=""></span>mm<span class="elsevierStyleSup">2</span> (SD<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.27). <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a> shows that the correlation between the estimated area and that measured by OCT exhibited differences between experts (which were significant for extreme values).</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><p id="par0090" class="elsevierStylePara elsevierViewall">In the absolute comparison of the area value highly significant differences were observed between some experts (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.0001) but not between others.</p><p id="par0095" class="elsevierStylePara elsevierViewall">It must be noted that experts 4 and 5 exhibited the best correlations with the OCT area and also were the ones which defined the smallest areas. Expert 2 defined the maximum area and exhibited the worst correlation. Expert 5 is one of the experts which exhibited the lowest variability vis-à-vis the mean radius.</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Discussion</span><p id="par0100" class="elsevierStylePara elsevierViewall">This study is the result of the drive for achieving precise and reproducible definitions of ONH limits.</p><p id="par0105" class="elsevierStylePara elsevierViewall">A new computer tool, accessible through the web, has been proposed for training and self-assessment of experts in this task. Said tool is an initial prototype as there are no references to similar systems in specialized literature. The tool proposed for ONH segmentation could be very useful for those who are in a learning period as well as for experts because the tool facilitates a more objective determination of ONH as it is always focused on a mean point with equidistant measurements.</p><p id="par0110" class="elsevierStylePara elsevierViewall">In what concerns its limitations, the graphic interface of the tool could be improved as well as the interpolation between points to improve visibility (by using curves instead of straight lines to join the points marked by the experts). A further improvement would consist in displaying a comparison of results between the points marked by the user and the gold standard established for that image. On the other hand, it would be convenient to enable inputting OCT data into the application so that it could gain experience and improve the gold standard. All these potential improvements are included in the future development of the application.</p><p id="par0115" class="elsevierStylePara elsevierViewall">The experience of this study gives rise to the conclusion that there is a tendency to oversize the ONH area when estimated by surface observation. Accordingly, this tendency should be reverted possibly not taking tissue color change as reference but the slightly more internal area where the vessels begin to bend. Consequently, the initial pattern we have chosen should be pondered toward the personal criteria followed by observer 5 when performing ONH definition as this expert is the one exhibiting the highest match with OCT.</p><p id="par0120" class="elsevierStylePara elsevierViewall">It also seems likely that the best match of experts in the task of defining the nasal and temporal contours is because in these areas papillary atrophies, pigmentation areas and other related areas appear with greater frequency and constitute obstacles for the precise detection of the edges.</p><p id="par0125" class="elsevierStylePara elsevierViewall">In the future, we expect to complete the training tool available in the RIM-ONE website, to propose a similar measure for intraobserver variability and to carry out a study of the relationship between contour definition variability and the papillary area.</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Funding</span><p id="par0130" class="elsevierStylePara elsevierViewall">This paper has been partially funded by the following projects: SolSubC200801000097 of the <span class="elsevierStyleGrantSponsor" id="gs0005">Research Agency of the Canary Islands</span>, <span class="elsevierStyleGrantSponsor" id="gs0010">Innovation and Information Society</span> and PI09/0601 of the <span class="elsevierStyleGrantSponsor" id="gs0015">Health Research Fund of the Carlos III Institute</span>, both with FEDER funds.</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Conflict of interests</span><p id="par0135" class="elsevierStylePara elsevierViewall">No conflict of interests has been declared by the authors.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:12 [ 0 => array:2 [ "identificador" => "xres208474" "titulo" => array:5 [ 0 => "Abstract" 1 => "Objective" 2 => "Method" 3 => "Results" 4 => "Conclusions" ] ] 1 => array:2 [ "identificador" => "xpalclavsec195756" "titulo" => "Keywords" ] 2 => array:2 [ "identificador" => "xres208473" "titulo" => array:5 [ 0 => "Resumen" 1 => "Objetivo" 2 => "Método" 3 => "Resultados" 4 => "Conclusiones" ] ] 3 => array:2 [ "identificador" => "xpalclavsec195757" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:2 [ "identificador" => "sec0010" "titulo" => "Subjects, materials and methods" ] 6 => array:2 [ "identificador" => "sec0015" "titulo" => "Results" ] 7 => array:2 [ "identificador" => "sec0020" "titulo" => "Discussion" ] 8 => array:2 [ "identificador" => "sec0025" "titulo" => "Funding" ] 9 => array:2 [ "identificador" => "sec0030" "titulo" => "Conflict of interests" ] 10 => array:2 [ "identificador" => "xack50342" "titulo" => "Acknowledgments" ] 11 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2011-07-27" "fechaAceptado" => "2012-07-10" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec195756" "palabras" => array:5 [ 0 => "Optic nerve" 1 => "Papilla" 2 => "Glaucoma" 3 => "Morphology" 4 => "Variability" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec195757" "palabras" => array:5 [ 0 => "Nervio óptico" 1 => "Papila" 2 => "Glaucoma" 3 => "Morfología" 4 => "Variabilidad" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:2 [ "titulo" => "Abstract" "resumen" => "<span class="elsevierStyleSectionTitle" id="sect0010">Objective</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Estimation of the error rate in the subjective determination of the optic nerve head edge and area.</p> <span class="elsevierStyleSectionTitle" id="sect0015">Method</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">(1) 169 images of optic nerve disk were evaluated by five experts for the defining of the edges in 8 positions (every 45°). (2) The estimated areas of 26 cases were compared with the measurements of the Cirrus Optical Coherence Tomography (OCT-Cirrus).</p> <span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">(1) The mean variation of the estimated radius was ±5.2%, with no significant differences between sectors. Specific differences were found between the 5 experts (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.001), each one compared with the others. (2) The disk area measured by the OCT-Cirros was 1.78<span class="elsevierStyleHsp" style=""></span>mm<span class="elsevierStyleSup">2</span> (SD<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.27). The results corresponding to the experts who detected smaller areas were better correlated to the area detected by the OCT-Cirrus (<span class="elsevierStyleItalic">r</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.77–0.88) than the results corresponding to larger areas (<span class="elsevierStyleItalic">r</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.61–0.69) (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05 in extreme cases).</p> <span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">There are specific patterns in each expert for defining the disk edges and involve 20% variation in the estimation of the optic nerve area. The experts who detected smaller areass have a higher agreement with the objective method used. A web tool is proposed for self-assessment and training in this task.</p>" ] "es" => array:2 [ "titulo" => "Resumen" "resumen" => "<span class="elsevierStyleSectionTitle" id="sect0035">Objetivo</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Estimar el grado de error en la determinación subjetiva del límite papilar.</p> <span class="elsevierStyleSectionTitle" id="sect0040">Método</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">(1) Fueron evaluadas 169 imágenes papilares por cinco expertos para delimitar los bordes papilares en 8 posiciones (cada 45°). (2) Las áreas estimadas en 26 casos se compararon con las medidas mediante tomógrafo de coherencia óptica (OCT-Cirrus).</p> <span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">(1) La variación media del radio papilar estimado fue de ±<span class="elsevierStyleHsp" style=""></span>5,2%, sin diferencias significativas entre sectores. Entre los cinco expertos existieron diferencias específicas (p<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0,001) de cada uno respecto a los restantes. (2) El área papilar medida por OCT-Cirrus fue de 1,78<span class="elsevierStyleHsp" style=""></span>mm<span class="elsevierStyleSup">2</span> (DE<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0,27). Los resultados de los expertos que informaron de áreas menores estuvieron mejor correlacionados con el área de OCT-Cirrus (<span class="elsevierStyleItalic">r</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0,77–0,88) que los que informaron de áreas mayores (r<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0,61–0,69) (p<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0,05 en casos extremos).</p> <span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Existen patrones específicos de cada experto para definir los límites papilares que pueden significar variaciones del 20% en la estimación de su área. Aquellos expertos que realizan delimitaciones menores tuvieron una mayor coincidencia con el método objetivo utilizado. Proponemos una herramienta web de autoevaluación y entrenamiento en esta tarea.</p>" ] ] "NotaPie" => array:2 [ 0 => array:2 [ "etiqueta" => "☆☆" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">This paper has been accepted for presentation in the 87 Congreso de la Sociedad Española de Oftalmología.</p>" ] 1 => array:2 [ "etiqueta" => "☆" "nota" => "<p class="elsevierStyleNotepara" id="npar0010">Please cite this article as: Alayon S., et al. Variabilidad entre expertos al delimitar el borde y área papilar. Arch Soc Esp Oftamol. 2013;88:168–73.</p>" ] ] "multimedia" => array:7 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 3004 "Ancho" => 2167 "Tamanyo" => 326791 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Preparing the pattern edge. (A) Reference point and directional axes 1–8; (B) measuring distances and (C) pattern: mean radii AR1–AR8 and reference point.</p>" ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1989 "Ancho" => 3000 "Tamanyo" => 292553 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Approximate definition of ONH with an ellipse.</p>" ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1990 "Ancho" => 3000 "Tamanyo" => 291269 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Precise adjustment of the papillary edge with uniformly distributed radii.</p>" ] ] 3 => array:7 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Expert \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">5 \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Mean \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−1.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−4.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−2.5 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Standard deviation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.8 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab334975.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Mean variability percentage between manual definitions of each expert and the pattern.</p>" ] ] 4 => array:7 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:2 [ "leyenda" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">R1, superior vertical (90°); R2, superior nasal (45°); R3, horizontal nasal (0°); R4, inferior nasal (315°); R5, inferior vertical (270°); R6, inferior temporal (225°); R7, horizontal temporal (180°); R8, superior temporal (135°).</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Radius \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">R1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">R2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">R3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">R4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">R5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">R6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">R7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">R8 \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Mean \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.3 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Standard deviation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.8 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.3 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab334976.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Mean variability percentage of all experts in each area.</p>" ] ] 5 => array:7 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" style="border-bottom: 2px solid black">Average \t\t\t\t\t\t\n \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Correl. coefficient (<span class="elsevierStyleItalic">r</span>) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.69 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.61 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.72 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.77 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.88 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.78 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">% of papillary area OCT \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">98.0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">109.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">108.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">91.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">92.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">100.0 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab334977.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Correlation between the papillary area estimated by the expert and that measured by optical coherence tomography and the mean variation of the area determined by each expert.</p>" ] ] 6 => array:5 [ "identificador" => "eq0005" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "CVi=abs(Ri−ARi)ARi×100, 1≤i≤8(%)" "Fichero" => "si1.jpeg" "Tamanyo" => 3115 "Alto" => 37 "Ancho" => 307 ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:20 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Automated segmentation of the optic nerve head from stereo color photographs using physiologically plausible feature detectors" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "M.D. Abràmoff" 1 => "W.L. 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Year/Month | Html | Total | |
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2023 March | 2 | 3 | 5 |
2018 February | 13 | 0 | 13 |
2018 January | 8 | 0 | 8 |
2017 December | 6 | 0 | 6 |
2017 November | 9 | 1 | 10 |
2017 October | 13 | 2 | 15 |
2017 September | 8 | 3 | 11 |
2017 August | 11 | 3 | 14 |
2017 July | 13 | 3 | 16 |
2017 June | 27 | 9 | 36 |
2017 May | 30 | 3 | 33 |
2017 April | 33 | 5 | 38 |
2017 March | 21 | 26 | 47 |
2017 February | 24 | 3 | 27 |
2017 January | 17 | 0 | 17 |
2016 December | 33 | 5 | 38 |
2016 November | 31 | 5 | 36 |
2016 October | 30 | 1 | 31 |
2016 September | 16 | 3 | 19 |
2016 August | 30 | 5 | 35 |
2016 July | 10 | 1 | 11 |
2016 June | 21 | 12 | 33 |
2016 May | 13 | 5 | 18 |
2016 April | 12 | 8 | 20 |
2016 March | 11 | 4 | 15 |
2016 February | 13 | 5 | 18 |
2016 January | 16 | 4 | 20 |
2015 December | 19 | 6 | 25 |
2015 November | 14 | 4 | 18 |
2015 October | 20 | 6 | 26 |
2015 September | 10 | 2 | 12 |
2015 August | 14 | 3 | 17 |
2015 July | 7 | 2 | 9 |
2015 June | 8 | 1 | 9 |
2015 May | 22 | 2 | 24 |
2015 April | 15 | 3 | 18 |
2015 March | 29 | 7 | 36 |
2015 February | 14 | 1 | 15 |
2015 January | 24 | 3 | 27 |
2014 December | 46 | 5 | 51 |
2014 November | 23 | 3 | 26 |
2014 October | 53 | 6 | 59 |
2014 September | 36 | 8 | 44 |
2014 August | 31 | 1 | 32 |
2014 July | 17 | 3 | 20 |
2014 June | 15 | 2 | 17 |
2014 May | 18 | 1 | 19 |
2014 April | 14 | 4 | 18 |
2014 March | 22 | 6 | 28 |
2014 February | 17 | 2 | 19 |
2014 January | 16 | 3 | 19 |
2013 December | 25 | 2 | 27 |
2013 November | 20 | 9 | 29 |
2013 October | 1 | 1 | 2 |
2013 September | 0 | 1 | 1 |