Prostate cancer detection and segmentation in multi-parametric mri via cnn and conditional random field

Ruiming Cao, Xinran Zhong, Sepideh Shakeri, Amirhossein Mohammadian Bajgiran, Sohrab Afshari Mirak, Dieter Enzmann, Steven S. Raman, Kyunghyun Sung

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

12 Scopus citations

Abstract

Multi-parametric MRI (mp-MRI) is a powerful diagnostic tool for prostate cancer (PCa). However, interpreting prostate mp-MRI requires high-level expertise, causing significant inter-reader variations. Convolutional neural networks (CNNs) have recently shown great promise for various tasks. In this study, we propose an improved CNN to jointly detect PCa lesions and segment for accurate lesions contours. Specifically, we adapt focal loss to overcome the imbalance between cancerous and non-cancerous areas for improved lesion detection and design selective dense conditional random field (SD-CRF), a post-processing step to refine the CNN prediction into the lesion segmentation based on a specific imaging component of mp-MRI. We trained and validated the proposed CNN in 5-fold cross-validation using 397 preoperative mp-MRI exams with whole-mount histopathology-confirmed lesion annotations. In the free-response receiver operating characteristics (FROC) analysis, the proposed CNN achieved 75.1% lesion detection sensitivity at the cost of 1 false positive per patient. In the evaluation for lesion segmentation, the proposed CNN improved the Dice coefficient by 20.6% from the baseline CNN.

Original languageEnglish (US)
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages1900-1904
Number of pages5
ISBN (Electronic)9781538636411
DOIs
StatePublished - Apr 2019
Externally publishedYes
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: Apr 8 2019Apr 11 2019

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2019-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Country/TerritoryItaly
CityVenice
Period4/8/194/11/19

Keywords

  • Computer-aided detection and diagnosis
  • Convolutional neural networks
  • MRI
  • Prostate cancer

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'Prostate cancer detection and segmentation in multi-parametric mri via cnn and conditional random field'. Together they form a unique fingerprint.

Cite this