Glomerulus Detection on Light Microscopic Images of Renal Pathology with the Faster R-CNN

Ying Chih Lo, Chia Feng Juang*, I. Fang Chung, Shin Ning Guo, Man Ling Huang, Mei Chin Wen, Cheng Jian Lin, Hsueh Yi Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

Glomerulus is an important component in human kidney. The appearance of the glomeruli on light microscopic image can provide abundant information for disease diagnosis. Due to the importance of glomeruli on accurate renal disease diagnosis, this paper proposes an automatic method to detect glomeruli in light microscopy images with Periodic Acid Schiff (PAS) or hematoxylin and eosin (H&E) stains at 100x, 200x, or 400x optical magnification. The faster region-based convolutional neural network (R-CNN) is applied to the detection task. The proposed detection approach performs an end-to-end glomerulus detection without any a priori information of the stains and magnifications of the images. The training dataset contains 2,511 images with 3,956 glomeruli. The test dataset contains 482 images with 563 glomeruli. The recall and precision of the test result are 91.54% and 86.50%, respectively, which shows the effectiveness of the proposed detection method.

Original languageEnglish
Title of host publicationNeural Information Processing - 25th International Conference, ICONIP 2018, Proceedings
EditorsSeiichi Ozawa, Andrew Chi Sing Leung, Long Cheng
PublisherSpringer Verlag
Pages369-377
Number of pages9
ISBN (Print)9783030042387
DOIs
StatePublished - 2018
Event25th International Conference on Neural Information Processing, ICONIP 2018 - Siem Reap, Cambodia
Duration: 13 Dec 201816 Dec 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11307 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Conference on Neural Information Processing, ICONIP 2018
Country/TerritoryCambodia
CitySiem Reap
Period13/12/1816/12/18

Keywords

  • Convolutional neural networks
  • Deep learning
  • Glomerulus detection
  • Region-based convolutional neural networks
  • Renal pathology

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