An improved deep learning-based MSA-Net model for small liver tumor segmentation

Abstract

Objective Liver cancer is one of the most fatal types of cancer in the world, and early identification is one of the most crucial considerations that may improve the likelihood of recovery. Hence, building a computer-aided diagnosis (CAD) system that improves radiologists’ operations by providing automated, high-precision small-tumor segmentation in complicated medical imaging is the main objective of this research study. Methods Conventional deep learning techniques, like U-Net, have shown impressive overall tumor delineation results, but they frequently struggle to distinguish between large and small tumors. The occurrence of tiny tumors and tumors with different shapes in the intricate liver exacerbates this deficiency. This paper proposed an MSA-Net (Multi-Scale Attention Network), a variant of the U-Net architecture, to address these problems. This is accomplished by the effective extraction of small-tumor features, which is made possible by the architecture’s integration of multi-scale convolutional layers into the encoder and decoder pathways, as well as an attention mechanism that concentrates on salient regions and gathers contextual data across several receptive fields. Results The proposed MSA-Net was trained and tested using the publicly accessible 3DIRCADb and LiTS datasets. The 3Dircadb dataset yielded a dice score of 92.00% and a Jaccard index of 86.00% for small tumors, whereas the LiTS dataset yielded a dice score of 72.57% and a Jaccard index of 65.35% for small tumors. Conclusion In contrast to other research, our technique assesses the outcomes by evaluating large and small tumors independently. The findings demonstrate that MSA-Net greatly improves segmentation, particularly in tiny tumors, demonstrating its usefulness in real-world clinical contexts.