Deep learning network for medical volume data segmentation based on multi axial plane fusion

Background and Objective: High-dimensional data generally contains more accurate information for med-ical image, e.g., computerized tomography (CT) data can depict the three dimensional structure of organs more precisely. However, the data in high-dimension often needs enormous computation and has high memory requirements in the deep learning convolution networks, while dimensional reduction usually leads to performance degradation. Methods: In this paper, a two-dimensional deep learning segmentation network was proposed for medical volume data based on multi-pinacoidal plane fusion to cover more in-formation under the control of computation.This approach has conducive compatibility while using the model proposed to extract the global information between different inputs layers. Results: Our approach has worked in different backbone network. Using the approach, DeepUnet's Dice coefficient (Dice) and Positive Predictive Value (PPV) are 0.883 and 0.982 showing the satisfied progress. Various backbones can enjoy the profit of the method. Conclusions: Through the comparison of different backbones, it can be found that the proposed network with multi-pinacoidal plane fusion can achieve better results both quantitively and qualitatively. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

A two-dimensional deep learning segmentation network based on multi-pinacoidal plane fusion was proposed for medical volume data, achieving satisfactory progress on different backbone networks. The approach covers more information and shows compatibility while extracting global information between different input layers.