Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains

Catherine P Jayapandian; Yijiang Chen; Andrew R Janowczyk; Matthew B Palmer; Clarissa A Cassol; Miroslav Sekulic; Jeffrey B Hodgin; Jarcy Zee; Stephen M Hewitt; John O'Toole; Paula Toro; John R Sedor; Laura Barisoni; Anant Madabhushi; Nephrotic Syndrome Study Network (NEPTUNE)

doi:10.1016/j.kint.2020.07.044

Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains

Kidney Int. 2021 Jan;99(1):86-101. doi: 10.1016/j.kint.2020.07.044. Epub 2020 Aug 22.

Authors

Collaborators

Nephrotic Syndrome Study Network (NEPTUNE):
J Sedor, K Dell, M Schachere, J Negrey, K Lemley, E Lim, T Srivastava, A Garrett, C Sethna, K Laurent, G Appel, M Toledo, L Barisoni, L Greenbaum, C Wang, C Kang, S Adler, C Nast, J LaPage, John H Stroger Jr, A Athavale, M Itteera, A Neu, S Boynton, F Fervenza, M Hogan, J Lieske, V Chernitskiy, F Kaskel, N Kumar, P Flynn, J Kopp, J Blake, H Trachtman, O Zhdanova, F Modersitzki, S Vento, R Lafayette, K Mehta, C Gadegbeku, D Johnstone, S Quinn-Boyle, D Cattran, M Hladunewich, H Reich, P Ling, M Romano, A Fornoni, C Bidot, M Kretzler, D Gipson, A Williams, J LaVigne, V Derebail, K Gibson, A Froment, S Grubbs, L Holzman, K Meyers, K Kallem, J Lalli, K Sambandam, Z Wang, M Rogers, A Jefferson, S Hingorani, K Tuttle, M Bray, M Kelton, A Cooper, B Freedman, J J Lin

Affiliations

¹ Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA. Electronic address: [email protected].
² Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
³ Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA; Precision Oncology Center, Lausanne University Hospital, Vaud, Switzerland.
⁴ Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
⁵ Department of Pathology, Ohio State University, Columbus, Ohio, USA.
⁶ Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA; Department of Pathology, University Hospitals of Cleveland, Cleveland, Ohio, USA.
⁷ Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.
⁸ Department of Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
⁹ Laboratory of Pathology, National Institutes of Health, National Cancer Institute, Bethesda, Maryland, USA.
¹⁰ Lerner Research and Glickman Urology and Kidney Institutes, Cleveland Clinic, Cleveland, Ohio, USA.
¹¹ Department of Pathology, Universidad Nacional de Colombia, Bogotá, Colombia.
¹² Lerner Research and Glickman Urology and Kidney Institutes, Cleveland Clinic, Cleveland, Ohio, USA; Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, Ohio, USA.
¹³ Department of Pathology and Medicine, Division of Nephrology, Duke University, Durham, North Carolina, USA.
¹⁴ Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA; Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio, USA.

Abstract

The application of deep learning for automated segmentation (delineation of boundaries) of histologic primitives (structures) from whole slide images can facilitate the establishment of novel protocols for kidney biopsy assessment. Here, we developed and validated deep learning networks for the segmentation of histologic structures on kidney biopsies and nephrectomies. For development, we examined 125 biopsies for Minimal Change Disease collected across 29 NEPTUNE enrolling centers along with 459 whole slide images stained with Hematoxylin & Eosin (125), Periodic Acid Schiff (125), Silver (102), and Trichrome (107) divided into training, validation and testing sets (ratio 6:1:3). Histologic structures were manually segmented (30048 total annotations) by five nephropathologists. Twenty deep learning models were trained with optimal digital magnification across the structures and stains. Periodic Acid Schiff-stained whole slide images yielded the best concordance between pathologists and deep learning segmentation across all structures (F-scores: 0.93 for glomerular tufts, 0.94 for glomerular tuft plus Bowman's capsule, 0.91 for proximal tubules, 0.93 for distal tubular segments, 0.81 for peritubular capillaries, and 0.85 for arteries and afferent arterioles). Optimal digital magnifications were 5X for glomerular tuft/tuft plus Bowman's capsule, 10X for proximal/distal tubule, arteries and afferent arterioles, and 40X for peritubular capillaries. Silver stained whole slide images yielded the worst deep learning performance. Thus, this largest study to date adapted deep learning for the segmentation of kidney histologic structures across multiple stains and pathology laboratories. All data used for training and testing and a detailed online tutorial will be publicly available.

Keywords: computerized morphologic assessment; deep learning; digital pathology; kidney histologic primitives; large-scale tissue interrogation; renal biopsy interpretation.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Biopsy
Coloring Agents
Deep Learning*
Kidney
Kidney Cortex / diagnostic imaging

Substances

Coloring Agents

Abstract

Publication types

MeSH terms

Substances

Grants and funding