Paper Info
Title
Automatic segmentation of histopathological slides of renal tissue using deep learning.
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, inflammatory 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 networks are able to accurately perform segmentation tasks in renal tissue, with accuracies of 90% for most classes.
Year
DOI
Venue
2018
10.1117/12.2293717
Proceedings of SPIE
Keywords
DocType
Volume
Renal tissue segmentation,computational pathology,data augmentation,convolutional neural networks
Conference
10581
ISSN
Citations 
PageRank 
0277-786X
1
0.37
References 
Authors
0
6
Name
Order
Citations
PageRank
Thomas de Bel1261.41
Meyke Hermsen220.71
Bart Smeets381.68
Luuk Hilbrands410.37
Jeroen van der Laak590.84
Geert Litjens699650.79