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Open AccessFeature PaperArticle

Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections

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Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy
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Department of Emergency and Organ Transplantation (DETO), Nephrology Unit, University of Bari Aldo Moro, 70126 Bari, Italy
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Author to whom correspondence should be addressed.
Electronics 2020, 9(3), 503; https://doi.org/10.3390/electronics9030503
Received: 28 February 2020 / Revised: 13 March 2020 / Accepted: 17 March 2020 / Published: 19 March 2020
(This article belongs to the Special Issue Biomedical Image Processing and Classification)
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. View Full-Text
Keywords: semantic segmentation; convolutional neural networks; kidney biopsy; kidney transplantation; glomerulus detection; glomerulosclerosis semantic segmentation; convolutional neural networks; kidney biopsy; kidney transplantation; glomerulus detection; glomerulosclerosis
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Altini, N.; Cascarano, G.D.; Brunetti, A.; Marino, F.; Rocchetti, M.T.; Matino, S.; Venere, U.; Rossini, M.; Pesce, F.; Gesualdo, L.; Bevilacqua, V. Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections. Electronics 2020, 9, 503.

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