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.
CT is widely used for diagnosis and treatment of a variety of diseases, including characterization of muscle loss. In many cases, changes in muscle mass, particularly abdominal muscle, indicate how well a patient is responding to treatment. Therefore, physicians use CT to monitor changes in muscle mass throughout the patient’s course of treatment. In order to measure the muscle, radiologists must segment and review each CT slice manually, which is a time-consuming task. In this work, we present a fully convolutional neural network (CNN) for the segmentation of abdominal muscle on CT. We achieved a mean Dice similarity coefficient of 0.92, a mean precision of 0.93, and a mean recall of 0.91 in an independent test set. The CNN-based segmentation method can provide an automatic tool for the segmentation of abdominal muscle. As a result, the time required to obtain information about changes in abdominal muscle using the CNN takes a fraction of the time associated with manual segmentation methods and thus can provide a useful tool in the clinical application.
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