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Proceedings Paper

Automatic segmentation of histopathological slides of renal tissue using deep learning
Author(s): Thomas de Bel; Meyke Hermsen; Bart Smeets; Luuk Hilbrands; Jeroen van der Laak; Geert Litjens
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Paper Abstract

Diagnoses in kidney disease often depend on quantification and presence of specific structures in the tissue. The progress in the field of whole-slide imaging and deep learning has opened up new possibilities for automatic analysis of histopathological slides. An initial step for renal tissue assessment is the differentiation and segmentation of relevant tissue structures in kidney specimens. We propose a method for segmentation of renal tissue using convolutional neural networks. Nine structures found in (pathological) renal tissue are included in the segmentation task: glomeruli, proximal tubuli, distal tubuli, arterioles, capillaries, sclerotic glomeruli, atrophic tubuli, in ammatory infiltrate and fibrotic tissue. Fifteen whole slide images of normal cortex originating from tumor nephrectomies were collected at the Radboud University Medical Center, Nijmegen, The Netherlands. The nine classes were sparsely annotated by a PhD student, experienced in the field of renal histopathology (MH). Experiments were performed with three different network architectures: a fully convolutional network, a multi-scale fully convolutional network and a U-net. We assessed the added benefit of combining the networks into an ensemble. We performed four-fold cross validation and report the average pixel accuracy per annotation for each class. Results show that convolutional neural net- works are able to accurately perform segmentation tasks in renal tissue, with accuracies of 90% for most classes.

Paper Details

Date Published: 6 March 2018
PDF: 6 pages
Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 1058112 (6 March 2018); doi: 10.1117/12.2293717
Show Author Affiliations
Thomas de Bel, Radboud Univ. Medical Ctr. (Netherlands)
Meyke Hermsen, Radboud Univ. Medical Ctr. (Netherlands)
Bart Smeets, Radboud Univ. Medical Ctr. (Netherlands)
Luuk Hilbrands, Radboud Univ. Medical Ctr. (Netherlands)
Jeroen van der Laak, Radboud Univ. Medical Ctr. (Netherlands)
Geert Litjens, Radboud Univ. Medical Ctr. (Netherlands)


Published in SPIE Proceedings Vol. 10581:
Medical Imaging 2018: Digital Pathology
John E. Tomaszewski; Metin N. Gurcan, Editor(s)

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