Automated Gastrointestinal Tract Image Segmentation Of Cancer Patient Using LeVit-UNet To Automate Radiotherapy

Abstract

Gastrointestinal(GI) tract cancer is a common type of cancer around the world. Cancer patients require radiotherapy as a part of a cancer diagnosis. To provide therapy in the cancer-affected GI tract, it needs to avoid the stomach and bowels because, in this case, the stomach and intestine are not cancer affected. It is ineffective to manually avoid the intestines and stomach and move the X-ray beam toward the cancer cell because it is a time-consuming, labor-intensive mechanism. Besides these issues, a patient feels uncomfortable while repeatedly X-ray beam is set manually. We implemented a deep learning-based automated medical image segmentation method using LeVit-UNet to overcome these issues. LeVit-UNet is a transformer-based architecture built using the Le Vit unit and CNN. The proposed system properly segments images into three classes: stomach, large, and small bowel. Three backbones of LeVit-UNet: Le Vit-128, Le Vit-192, Le Vit-384 were used in our research. Validation loss, dice score, and IOU were generated and recorded to evaluate all models using three backbones. Though Le Vit-UNet-384 performs well, in our research work, LeVit-UNet-192 performed best.