Influence of applied corneal endothelium image segmentation techniques on the clinical parameters

Piórkowski, Adam; Nurzynska, Karolina; Gronkowska-Serafin, Jolanta; Selig, Bettina; Boldak, Cezary; Reska, Daniel

doi:10.1016/j.compmedimag.2016.07.010

Cited by 30 publications

(26 citation statements)

References 43 publications

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“…Their aim is to delineate cell borders using such techniques as: local greyscale thresholding followed by scissoring and morphological thinning [4], [5], scale-space filtering followed by binarization and morphological processing [6] or hexagon detection using shape dependent filters [7], [8], [9]. More sophisticated methods include application of watersheds [10], [11], [12], [13], [14], active contours [15], [16], genetic algorithms [17] or analysis of local pixel levels aimed at finding intensity valleys corresponding to borders between cells [18]. Several machine learning approaches have also been proposed by the team of Ruggeri, including: neural network [19], [20], Bayesian framework [21], support vector machines classifier [22] and genetic algorithm [23].…”

Section: Introductionmentioning

confidence: 99%

Corneal Endothelium Image Segmentation Using Feedforward Neural Network

Fabijańska

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-In this paper the problem of corneal endothelium image segmentation is considered. Particularly, a fully automatic approach for delineating contours of corneal endothelial cells is proposed. The approach produces one pixel width outline of cells. It bases on a simple feedforward neural network trained to recognize pixels which belong to the cell borders. The edge probability (edginess) map output by the network is next analysed row by row and column by column in order to find local peaks of the network response. These peaks are considered as cell border candidates and in the last step of the method via binary morphological processing are linked to create continuous outlines of cells. The results of applying the proposed approach to publicity available data set of corneal endothelium images as well as the assessment of the method against ground truth segmentation are presented and discussed. Obtained results show, that the proposed approach performs very well. The resulting mean absolute error of cell number determination is around 5% while the average DICE measure reaches 0.83 which is a good result, especially when one pixel width objects are compared.

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Section: Introductionmentioning

confidence: 99%

Corneal Endothelium Image Segmentation Using Feedforward Neural Network

Fabijańska

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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“…The most common approaches for nuclei segmentation are based on active contours, intensity thresholding, mathematical morphology, region growing, watershed, and deep learning [4,6,10,11,18]. Last years brought an enormous progress in classification and object recognition using Convolutional Neural Networks (CNN) [2,29].…”

Section: Introductionmentioning

confidence: 99%

Cell Nuclei Segmentation in Cytological Images Using Convolutional Neural Network and Seeded Watershed Algorithm

et al. 2019

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Morphometric analysis of nuclei is crucial in cytological examinations. Unfortunately, nuclei segmentation presents many challenges because they usually create complex clusters in cytological samples. To deal with this problem, we are proposing an approach, which combines convolutional neural network and watershed transform to segment nuclei in cytological images of breast cancer. The method initially is preprocessing images using color deconvolution to highlight hematoxylin-stained objects (nuclei). Next, convolutional neural network is applied to perform semantic segmentation of preprocessed image. It finds nuclei areas, cytoplasm areas, edges of nuclei, and background. All connected components in the binary mask of nuclei are treated as potential nuclei. However, some objects actually are clusters of overlapping nuclei. They are detected by their outlying values of morphometric features. Then an attempt is made to separate them using the seeded watershed segmentation. If the attempt is successful, they are included in the nuclei set. The accuracy of this approach is evaluated with the help of referenced, manually segmented images. The degree of matching between reference nuclei and discovered objects is measured with the help of Jaccard distance and Hausdorff distance. As part of the study, we verified how the use of a convolutional neural network instead of the intensity thresholding to generate a topographical map for the watershed improves segmentation outcomes. Our results show that convolutional neural network outperforms Otsu thresholding and adaptive thresholding in most cases, especially in scenarios with many overlapping nuclei.

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“…The problem to find an accurate delineation of cell boundaries is not a trivial task as it was argued in [36] and standard techniques of skeletonization are not sufficient. It was suggested [28] that a best-fit method solves all shortcomings and thus is exploited as a final skeletonization method applied for automatically obtained segmentation.…”

Section: Border Skeletonizationmentioning

confidence: 99%

“…It is difficult to find the objective measure, which enables comparison between two mosaics [36,37]. Therefore, here we investigate several approaches to better understand the quality of achieved results.…”

Section: Segmentation Accuracymentioning

confidence: 99%

Deep Learning as a Tool for Automatic Segmentation of Corneal Endothelium Images

Nurzynska

2018

Symmetry

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Abstract:The automatic analysis of the state of the corneal endothelium is of much interest in ophthalmology. Up till now, several manual and semi-automatic methods have been introduced, but the need of fully-automatic segmentation of cells in the endothelium is still in search. This work addresses the problem of automatic delineation of cells in the corneal endothelium images and suggests to use the convolutional neural network (CNN) to classify between cell center, cell body, and cell border in order to achieve precise segmentation. Additionally, a method to automatically select and split merged cells is given. In order to skeletonize the result, the best-fit method is used. The achieved outcomes are compared to manual annotations in order to define the mutual overlapping. The Dice index, Jaccard coefficient, modified Hausdorff distance, and several other metrics for mosaic overlapping are used. As a final check-up, the visual inspection is shown. The performed experiments revealed the best architecture for CNN. The correctness and precision of the segmentation were evaluated on Endothelial Cell "Alizarine" dataset. According to the Dice index and Jaccard coefficient, the automatically achieved cell delineation overlaps the original one with 93% precision. While modified Hausdorff distance shows 0.14 pixel distance, proving very high accuracy. These findings are confirmed by other metrics and also supported by presented visual inspection of achieved segmentations. To conclude, the methodology to achieve fully-automatic delineation of cell boundaries in the corneal endothelium images was presented. The segmentation obtained as a result of pixel classification with CNN proved very high precision.

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Influence of applied corneal endothelium image segmentation techniques on the clinical parameters

Cited by 30 publications

References 43 publications

Corneal Endothelium Image Segmentation Using Feedforward Neural Network

Corneal Endothelium Image Segmentation Using Feedforward Neural Network

Cell Nuclei Segmentation in Cytological Images Using Convolutional Neural Network and Seeded Watershed Algorithm

Deep Learning as a Tool for Automatic Segmentation of Corneal Endothelium Images

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