EM-Net: An Efficient M-Net for segmentation of surgical instruments in colonoscopy frames

Sammendrag

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.