EM-Net: An Efficient M-Net for segmentation of surgical instruments in colonoscopy frames
Keywords:Deep learning; Segmentation; Colonoscopy; Surgical instruments
This paper addresses the Instrument Segmentation Task, a subtask for the “MedAI: Transparency in Medical Image Segmentation” challenge. To accomplish the subtask, our team “Med_Seg_JU” has proposed a deep learning-based framework, namely “EM-Net: An Efficient M-Net for segmentation of surgical instruments in colonoscopy frames”. The proposed framework is inspired by the M-Net architecture. In this architecture, we have incorporated the EfficientNet B3 module with U-Net as the backbone. Our proposed method obtained a JC of 0.8205, DSC of 0.8632, PRE of 0.8464, REC of 0.9005, F1 of 0.8632, and ACC of 0.9799 as evaluated by the challenge organizers on a separate test dataset. These results justify the efficacy of our proposed method in the segmentation of the surgical instruments.