Abstract
The evaluation of kidney biopsies performed by expert pathologists is a crucial process for assessing if a kidney is eligible for transplantation. In this evaluation process, an important step consists of the quantification of global glomerulosclerosis, which is the ratio between sclerotic glomeruli and the overall number of glomeruli. Since there is a shortage of organs available for transplantation, a quick and accurate assessment of global glomerulosclerosis is essential for retaining the largest number of eligible kidneys. In the present paper, the authors introduce a Computer-Aided Diagnosis (CAD) system to assess global glomerulosclerosis. The proposed tool is based on Convolutional Neural Networks (CNNs). In particular, the authors considered approaches based on Semantic Segmentation networks, such as SegNet and DeepLab v3+. The dataset has been provided by the Department of Emergency and Organ Transplantations (DETO) of Bari University Hospital, and it is composed of 26 kidney biopsies coming from 19 donors. The dataset contains 2344 non-sclerotic glomeruli and 428 sclerotic glomeruli. The proposed model consents to achieve promising results in the task of automatically detecting and classifying glomeruli, thus easing the burden of pathologists. We get high performance both at pixel-level, achieving mean F-score higher than 0.81, and Weighted Intersection over Union (IoU) higher than 0.97 for both SegNet and Deeplab v3+ approaches, and at object detection level, achieving 0.924 as best F-score for non-sclerotic glomeruli and 0.730 as best F-score for sclerotic glomeruli.
Highlights
The spread of Deep Learning (DL) techniques and frameworks has led to a revolution in the medical imaging field
The increasing availability of whole-slide scanners, which facilitate the digitization of histopathological tissue, led to a new research field denoted as digital pathology and generated a strong demand for the development of Computer-Aided Diagnosis (CAD) systems
For the semantic segmentation task we group the metrics in Dataset Metrics and Class Metrics [33]
Summary
The spread of Deep Learning (DL) techniques and frameworks has led to a revolution in the medical imaging field. The development of new techniques able to objectively and rapidly interpret donor kidney biopsy to support pathologist’s decision making is strongly fostered. As stated in the literature, the application of deep learning techniques for the analysis of Whole-Slide Images (WSIs) has shown significant results and suggest that the integration of DL framework with CAD systems is a valuable solution. In object detection confusion matrices B, NS, S stand for Background, Non-sclerotic and Sclerotic, respectively. The object detection confusion matrices for SegNet and DeepLab v3+ are reported in Tables 10 and 11, respectively. The DeepLab v3+-based model obtained a better F-score for non-sclerotic glomeruli and a slightly worse F-score for sclerotic glomeruli
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