In Proceedings of SPIE--the International Society for Optical Engineering
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.
Chaudhary Usamah, Leitch Ka’Toria N, Chhabra Avneesh, Kohli Ajay, Fei Baowei
2022
Abdominal imaging, CT, convolutional neural network, machine learning, surgical patient outcomes
