HPM-Net: Hierarchical progressive multiscale network for liver vessel segmentation in CT images

Area of Science:

• Medical Imaging
• Radiology
• Computer-Aided Diagnosis

Background:

• Accurate segmentation of liver vessels in 3D CT images is crucial for diagnosing and planning treatments for liver diseases.
• Challenges include irregular vessel structures, image noise, and information loss during down-sampling, especially for small vessels.

Purpose of the Study:

• To develop an advanced deep learning framework for precise liver vessel segmentation.
• To address the limitations of existing methods in capturing fine vessel details and preventing information loss.

Main Methods:

• Proposed a hierarchical progressive multiscale learning network (HPM-Net) incorporating internal and external progressive learning.
• Introduced a dual-branch progressive 3D Unet with a dual-branch progressive (DBP) down-sampling strategy to preserve detailed information.
• Implemented a deep supervision mechanism to accelerate convergence and enhance network training.

Main Results:

• The HPM-Net achieved an average Dice coefficient of 75.18% and a sensitivity of 78.84% on the 3Dircadb dataset.
• These results indicate superior performance compared to the original network in liver vessel segmentation.

Conclusions:

• The proposed HPM-Net demonstrates significant accuracy in segmenting liver vessels from CT images.
• The method effectively overcomes challenges associated with vessel irregularity and information loss during segmentation.