Automated kidney segmentation by mask R-CNN in T2-weighted magnetic resonance imaging

Manu Goyal, Junyu Guo, Lauren Hinojosa, Keith Hulsey, Ivan Pedrosa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Despite the recent advances of deep learning algorithms in medical imaging, automatic segmentation algorithms for kidneys in Magnetic Resonance Imaging (MRI) examinations are lacking. Automated segmentation of kidneys in MRI can enable several clinical applications and use of radiomics and machine learning analysis of renal disease. In this work, we propose the application of a Mask R-CNN for the automatic segmentation of the kidneys in coronal T2- weighted single-shot fast spin echo MRI. We propose the morphological operations as post-processing to further improve the performance of Mask R-CNN for this task. With 5-fold cross-validation data, the proposed Mask R-CNN was trained and validated on 70 and 10 MRI exams, respectively, and then evaluated on the remaining 20 exams in each fold. Our proposed method achieved a dice score of 0.905 and Intersection over Union of 0.828.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2022
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKaren Drukker, Khan M. Iftekharuddin
PublisherSPIE
ISBN (Electronic)9781510649415
DOIs
StatePublished - 2022
EventMedical Imaging 2022: Computer-Aided Diagnosis - Virtual, Online
Duration: Mar 21 2022Mar 27 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12033
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2022: Computer-Aided Diagnosis
CityVirtual, Online
Period3/21/223/27/22

Keywords

  • Deep Learning
  • Dice.
  • Kidney
  • Mask R-CNN
  • Segmentation
  • T2-weighted MRI

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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