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Proceedings Paper

Fully automated classification of glomerular lesions in lupus nephritis
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Paper Abstract

Systemic lupus erythematosus is a disease in which the immune system attacks host tissues. One organ commonly attacked is the kidney, in which case the resultant acute and chronic damages are called lupus nephritis. The accumulated damage can result in renal failure. The percutaneous renal biopsy is invaluable to the assessment of the disease and its therapeutic response. A large portion of the pathological assessment is done by histological analysis of the biopsied tissue with light microscopy. Computational models can alleviate a portion of expert disagreement by providing unified, reproducible quantifications of digitized image structures. In this work, we perform fully automated whole slide segmentation of glomeruli from Periodic Acid- Schiff (PAS), hematoxylin and eosin, silver, and trichrome stained lupus nephritis biopsies. The automatically extracted PAS glomeruli are quantified by a set of 285 hand-crafted features designed specifically to target glomerular lesions in lupus nephritis. These features are fed in sequence to a recurrent neural network architecture which views multiple glomerular features from a single biopsy, and outputs a continuous diagnostic value representative of classes II-V of the scheme by Weening et al1. On 82 whole slide images taken from 65 patients, compared to renal pathologist annotations and using only the PAS stain, the network achieved a Cohen’s kappa of 0.42 with 95% confidence interval [0.32, 0.52] to render the correct class chosen from II-V, and 0.56, 95% CI [0.43, 0.69] to render an additional class V diagnosis when required.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200Y (16 March 2020); doi: 10.1117/12.2548528
Show Author Affiliations
Brandon Ginley, The State Univ. of New York, Buffalo (United States)
Kuang-Yu Jen, Univ. of California, Davis (United States)
Avi Rosenberg, Johns Hopkins Univ. School of Medicine (United States)
Giovanni Maria Rossi, Johns Hopkins Univ. School of Medicine (United States)
Univ. di Parma (Italy)
Sanjay Jain, Washington Univ. School of Medicine in St. Louis (United States)
Pinaki Sarder, The State Univ. of New York, Buffalo (United States)

Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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