V-Net Light - Parameter-Efficient 3-D Convolutional Neural Network for Prostate MRI Segmentation

Ophir Yaniv, Orith Portnoy, Amit Talmon, Nahum Kiryati, Eli Konen, Arnaldo Mayer

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

Abstract

Prostate MRI segmentation has become an important tool for quantitative estimation of the gland volume during diagnostic imaging. It is also a critical step in the fusion between MRI and transrectal ultrasound (TRUS) for fusion guided biopsy or therapy. 3-D neural networks have demonstrated strong potential for this task, but require substantial computational resources due to their large number of parameters. In this work, we focus on the efficiency of the segmentation network in terms of speed and memory requirements. Specifically, we aim at reaching state-of-the-art results with smaller networks, involving significantly fewer parameters, thus making the network easier to train and operate. A novel 3-D network architecture, called V-net Light (VnL) is proposed, based on an efficient 3-D Module called 3-D Light, that minimizes the number of network parameters while maintaining state-of-the-art segmentation results. The proposed method is validated on the PROMISE12 challenge data [1]. The proposed VnL has only 9.1% of V-net's parameters, 3.2% of its floating point operations (FLOPs) and uses only 9.1% of hard-disk storage compared to V-net, yet V-net and VnL has comparable accuracy.

Original languageEnglish
Title of host publicationISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages442-445
Number of pages4
ISBN (Electronic)9781538693308
DOIs
StatePublished - Apr 2020
Event17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States
Duration: 3 Apr 20207 Apr 2020

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2020-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
Country/TerritoryUnited States
CityIowa City
Period3/04/207/04/20

Keywords

  • Deep learning
  • Fast training
  • Neural network
  • Prostate segmentation
  • Small model

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