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

doi:10.1117/12.2611773

4 April 2022 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

Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 120361N (2022) https://doi.org/10.1117/12.2611773
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

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.

Conference Presentation

Citation Download Citation

Usamah Chaudhary, Ka'Toria N. Leitch, Avneesh Chhabra, Ajay Kohli, and Baowei Fei "Deep learning-based abdominal muscle segmentation on CT images of surgical patient populations", Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 120361N (4 April 2022); https://doi.org/10.1117/12.2611773

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available