Attention Enabled MultiResUNet for Bio-Medical Image Segmentation

This work introduces an innovative approach to medical image segmentation by integrating attention mechanisms into an advanced UNet called MultiResUNet architecture. The core objective is to investigate whether it can significantly improve the accuracy and efficiency of segmentation tasks in medical imaging, a critical component in diagnostic processes and treatment planning. The MultiResUNet model, known for its effectiveness in capturing multi-resolution features, is augmented with attention gates (AGs) to selectively emphasize salient features and suppress irrelevant areas in medical images. This integration addresses the challenge of varying object scales and complexities often encountered in medical datasets. By directing the model's focus to pertinent regions, the attention-gated MultiResUNet should demonstrate enhanced precision in delineating diverse anatomical structures and pathological regions compared to traditional segmentation models. This advancement in segmentation technology holds promise for providing more reliable and detailed analyses in medical applications, ultimately contributing to improved patient outcomes and more informed clinical decisions.

I. Introduction

Medical image analysis with Computer-aided tools primarily emphasizes two objectives: segmentation and diagnosis. The overarching goal of Semantic Segmentation involves dividing an image into distinct, non-overlapping regions [1]. This process combines pixels with similar characteristics. In medical imaging, the emphasis typically shifts to identifying certain areas of interest, such as tumor regions or various organs. This selective segmentation is crucial as it allows healthcare professionals to concentrate on the most relevant sections of complex multimodal medical images. Moreover, the segmented images often serve as a basis for extracting various features, which are instrumental in the diagnostic process.