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

Publication ,  Journal Article
Jayapandian, CP; Chen, Y; Janowczyk, AR; Palmer, MB; Cassol, CA; Sekulic, M; Hodgin, JB; Zee, J; Hewitt, SM; O'Toole, J; Toro, P; Sedor, JR ...
Published in: Kidney Int
January 2021

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.

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Published In

Kidney Int

DOI

EISSN

1523-1755

Publication Date

January 2021

Volume

99

Issue

1

Start / End Page

86 / 101

Location

United States

Related Subject Headings

  • Urology & Nephrology
  • Kidney Cortex
  • Kidney
  • Deep Learning
  • Coloring Agents
  • Biopsy
  • 3202 Clinical sciences
  • 1103 Clinical Sciences
 

Citation

APA
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ICMJE
MLA
NLM
Jayapandian, C. P., Chen, Y., Janowczyk, A. R., Palmer, M. B., Cassol, C. A., Sekulic, M., … Nephrotic Syndrome Study Network (NEPTUNE), . (2021). Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains. Kidney Int, 99(1), 86–101. https://doi.org/10.1016/j.kint.2020.07.044
Jayapandian, Catherine P., Yijiang Chen, Andrew R. Janowczyk, Matthew B. Palmer, Clarissa A. Cassol, Miroslav Sekulic, Jeffrey B. Hodgin, et al. “Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains.Kidney Int 99, no. 1 (January 2021): 86–101. https://doi.org/10.1016/j.kint.2020.07.044.
Jayapandian CP, Chen Y, Janowczyk AR, Palmer MB, Cassol CA, Sekulic M, et al. 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.
Jayapandian, Catherine P., et al. “Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains.Kidney Int, vol. 99, no. 1, Jan. 2021, pp. 86–101. Pubmed, doi:10.1016/j.kint.2020.07.044.
Jayapandian CP, Chen Y, Janowczyk AR, Palmer MB, Cassol CA, Sekulic M, Hodgin JB, Zee J, Hewitt SM, O’Toole J, Toro P, Sedor JR, Barisoni L, Madabhushi A, Nephrotic Syndrome Study Network (NEPTUNE). 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.
Journal cover image

Published In

Kidney Int

DOI

EISSN

1523-1755

Publication Date

January 2021

Volume

99

Issue

1

Start / End Page

86 / 101

Location

United States

Related Subject Headings

  • Urology & Nephrology
  • Kidney Cortex
  • Kidney
  • Deep Learning
  • Coloring Agents
  • Biopsy
  • 3202 Clinical sciences
  • 1103 Clinical Sciences