Deep convolutional neural networks for the automated segmentation of malignant pleural mesothelioma on computed tomography scans.

Abstract

Tumor volume has been a topic of interest in the staging, prognostic evaluation, and treatment response assessment of malignant pleural mesothelioma (MPM). Deep convolutional neural networks (CNNs) were trained separately for the left and right hemithoraces on the task of differentiating between pleural thickening and normal thoracic tissue on computed tomography (CT) scans. A total of 4259 and 6192 axial sections containing segmented tumor were used to train the left-hemithorax CNN and the right-hemithorax CNN, respectively. Two distinct test sets of 131 sections from the CT scans of 43 patients were used to evaluate segmentation performance by calculating the Dice similarity coefficient (DSC) between deep CNN-generated tumor segmentations and reference tumor segmentations provided by a total of eight observers. Median DSC values ranged from 0.662 to 0.800 over the two test sets when comparing deep CNN-generated segmentations with observer reference segmentations. The deep CNN-based method achieved significantly higher DSC values for all three observers on the test set that allowed direct comparisons with a previously published automated segmentation method of MPM tumor on CT scans ( ). A deep CNN was implemented for the automated segmentation of MPM tumor on CT scans, showing superior performance to a previously published method.