Semantic Segmentation of Gastrointestinal Tract in MRI Scans Using PSPNet Model With ResNet34 Feature Encoding Network

Gastrointestinal (GI) cancer is the most common cancer in men and women. GI cancers are increasing every year worldwide. In the biomedical industry, Radiation treatment is a frequent choice for treating cancers of the GI tract in which the oncologist focuses the high range of X-ray beams on the tumor while avoiding the healthy organs. Manual segmentation of healthy organs to focus X-ray beams only on the tumor portion is very tedious and time-consuming, which can lead the treatment from a few minutes to hours. Deep learning techniques can concur with this problem by segmentation of healthy organs. This research article proposes a deep learning-based model Pyramid Scene Parsing Network (PSPNet) for segmenting organs such as the stomach, small bowel, and large bowel in the GI tract. The model has been simulated with five feature encoding networks: ResNext 50, Timm_Gernet_S, ResNet 34, EfficientNet B1, and MobileNet V2. These encoders were used for downsampling the feature map in the PSPNet architecture. The implementations have been performed using the UW Madison GI tract dataset, which contains 38,496 MRI scans of cancer patients. The model was evaluated using validation dice, Jaccard, and validation loss. The results reveal that the PSPNet model combined with ResNet 34 as encoder outperforms the other feature encoding networks with validation dice as 0.8842, validation Jaccard as 0.8531, and validation loss as 0.1365. Radiation oncologists can use the proposed model to speed up radiation therapy for cancer treatment.