Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessor

TL;DR

This paper introduces TransFusionNet, a Transformer-based multi-scale feature fusion network that significantly improves liver tumor and vessel segmentation accuracy in CT images, and demonstrates its deployment on embedded microprocessors for automated surgical applications.

Contribution

The paper presents a novel Transformer-based network, TransFusionNet, optimized for embedded deployment, achieving state-of-the-art segmentation accuracy for liver tumors and vessels.

Findings

TransFusionNet achieved mean Dice of 0.899 for vessel segmentation.

TransFusionNet achieved mean Dice of 0.961 for liver tumor segmentation.

The embedded deployment demonstrates practical application in automated surgical tasks.

Abstract

Liver cancer is one of the most common malignant diseases in the world. Segmentation and labeling of liver tumors and blood vessels in CT images can provide convenience for doctors in liver tumor diagnosis and surgical intervention. In the past decades, many state-of-the-art medical image segmentation algorithms appeared during this period. With the development of embedded devices, embedded deployment for medical segmentation and automatic reconstruction brings prospects for future automated surgical tasks. Yet, most of the existing segmentation methods mostly care about the spatial feature context and have a perception defect in the semantic relevance of medical images, which significantly affects the segmentation accuracy of liver tumors and blood vessels. Deploying large and complex models into embedded devices requires a reasonable trade-off between model accuracy, reasoning speed and model capacity. Given these problems, we introduce a multi-scale feature fusion network called TransFusionNet based on Transformer. This network achieved very competitive performance for liver vessel and liver tumor segmentation tasks, meanwhile it can improve the recognition of morphologic margins of liver tumors by exploiting the global information of CT images. Experiments show that in vessel segmentation task TransFusionNet achieved mean Dice coefficients of 0.899 and in liver tumor segmentation task TransFusionNet achieved mean Dice coefficients of 0.961. Compared with the state-of-the-art framework, our model achieves the best segmentation result. In addition, we deployed the model into an embedded micro-structure and constructed an integrated model for liver tumor vascular segmentation and reconstruction. This proprietary structure will be the exclusive component of the future medical field.