Evidence map›Paper›PMID 42084803›Full record

ArticleBrain informatics2026

A deep-learning framework for brain tumor segmentation via three-dimensional mass-preserving geometric transformation.

Tsung-Ming Huang, Kai-Qian Zheng, Wen-Wei Lin, Tiexiang Li, Shing-Tung Yau

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Article in Brain informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Tsung-Ming HuangDepartment of Mathematics, National Taiwan Normal University, Taipei, 116, Taiwan. min@ntnu.edu.tw.
Kai-Qian ZhengDepartment of Applied Mathematics, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan.
Wen-Wei LinDepartment of Applied Mathematics, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan.
Tiexiang LiShanghai Institute for Mathematics and Interdisciplinary Sciences, Shanghai, China.
Shing-Tung YauYau Mathematical Sciences Center, Tsinghua University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article presents a robust and efficient framework for brain tumor segmentation based on deep learning. We introduce a novel three-dimensional (3D) mass-preserving geometric transformation (MPGT) that employs a homotopy method to transform irregular brain magnetic resonance (MR) images into standardized solid cubes. This transformation preserves local mass ratios while maintaining global structural integrity, providing a structured input for deep learning models. Furthermore, we propose a modified two-phase segmentation strategy to minimize inference time and a postprocessing technique to enhance lesion-wise performance. Extensive validation on the Brain Tumor Segmentation (BraTS) Challenge 2023 dataset demonstrates that our method, when integrated with nnU-Net, achieves competitive Dice scores of 0.9282 (Whole Tumor), 0.8812 (Tumor Core), and 0.8527 (Enhanced Tumor). These results are superior to or comparable with top-ranking competition entries.

Indexed as

3D medical imagingBrain tumor segmentationGeometric transformationHomotopy methodMass-preserving parameterizationnnU-NetTwo-phase segmentation

Identifiers

PMID42084803
PMCPMC13158315

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.