Automatic segmentation of colorectal cancer in 3D MRI by combining deep learning and 3D level-set algorithm-a preliminary study

In this paper, a novel method to automatically segment colorectal cancer from 3D MR images based on combination of 3D fully convolutional neural networks (3D-FCNNs) and 3D level-set is proposed. The 3D-level set is incorporated in the 3D-FCNNs aiming at: i) a fine-tuning of the training phase; ii) a refinement of the outputs during the testing phase by integrating smoothing function and prior information in a post-processing step. The proposed method is assessed and compared with 3D-FCNNs without 3D-level set (3D-FCNNs alone) in terms of Dice Similarity Coefficient (DSC) as a performance metric. The proposed method showed higher DSC than 3D-FCNNs alone on both training and testing data set as, (0.91813 vs 0.8568) and (0.9378 vs 0.86238), respectively. Our results on 3D colorectal MRI data demonstrated that the proposed method gives better and accurate segmentation results than 3D-FCNNs alone.