Deep Learning-Based Abdominal Muscle Segmentation on CT Images of Surgical Patient Populations

Usamah Chaudhary, Ka Toria N. Leitch, Avneesh Chhabra, Ajay Kohli, Baowei Fei

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

Abstract

Computed tomography (CT) is commonly used for the characterization and tracking of abdominal muscle mass in surgical patients for both pre-surgical outcome predictions and post-surgical monitoring of response to therapy. In order to accurately track changes of abdominal muscle mass, radiologists must manually segment CT slices of patients, a time-consuming task with potential for variability. In this work, we combined a fully convolutional neural network (CNN) with high levels of preprocessing to improve segmentation quality. We utilized a CNN based approach to remove patients’ arms and fat from each slice and then applied a series of registrations with a diverse set of abdominal muscle segmentations to identify a best fit mask. Using this best fit mask, we were able to remove many parts of the abdominal cavity, such as the liver, kidneys, and intestines. This preprocessing was able to achieve a mean Dice similarity coefficient (DSC) of 0.53 on our validation set and 0.50 on our test set by only using traditional computer vision techniques and no artificial intelligence. The preprocessed images were then fed into a similar CNN previously presented in a hybrid computer vision-artificial intelligence approach and was able to achieve a mean DSC of 0.94 on testing data. The preprocessing and deep learning-based method is able to accurately segment and quantify abdominal muscle mass on CT images.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2022
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsBarjor S. Gimi, Andrzej Krol
PublisherSPIE
ISBN (Electronic)9781510649477
DOIs
StatePublished - 2022
EventMedical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging - Virtual, Online
Duration: Mar 21 2022Mar 27 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12036
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging
CityVirtual, Online
Period3/21/223/27/22

Keywords

  • Abdominal imaging
  • convolutional neural network
  • CT
  • machine learning
  • surgical patient outcomes

ASJC Scopus subject areas

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

Fingerprint

Dive into the research topics of 'Deep Learning-Based Abdominal Muscle Segmentation on CT Images of Surgical Patient Populations'. Together they form a unique fingerprint.

Cite this