Brain tumor segmentation in MRI with multi-modality spatial information enhancement and boundary shape correction

作者全名：Zhu, Zhiqin; Wang, Ziyu; Qi, Guanqiu; Mazur, Neal; Yang, Pan; Liu, Yu

作者地址：[Zhu, Zhiqin; Wang, Ziyu] Chongqing Univ Posts & Telecommun, Coll Automat, Chongqing 400065, Peoples R China; [Qi, Guanqiu; Mazur, Neal] SUNY Buffalo, Comp Informat Syst Dept, Buffalo, NY 14222 USA; [Yang, Pan] Chongqing Med Univ, Emergency Dept, Affiliated Hosp 2, Chongqing 400013, Peoples R China; [Yang, Pan] Univ Chinese Acad Sci, Chongqing Gen Hosp, Dept Cardiovasc Surg, Chongqing 400013, Peoples R China; [Liu, Yu] Hefei Univ Technol, Dept Biomed Engn, Hefei 230009, Peoples R China

通信作者：Liu, Y (通讯作者)，Hefei Univ Technol, Dept Biomed Engn, Hefei 230009, Peoples R China.

来源：PATTERN RECOGNITION

ESI学科分类：ENGINEERING

WOS号：WOS:001240843000001

JCR分区：Q1

影响因子：7.5

年份：2024

卷号：153

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Brain tumor segmentation; Multi-modality MRI; Spatial information enhancement; Boundary shape correction

摘要：Brain tumor segmentation is currently of a priori guiding significance in medical research and clinical diagnosis. Brain tumor segmentation techniques can accurately partition different tumor areas on multi-modality images captured by magnetic resonance imaging (MRI). Due to the unpredictable pathological process of brain tumor generation and growth, brain tumor images often show irregular shapes and uneven internal gray levels. Existing neural network-based segmentation methods with an encoding/decoding structure can perform image segmentation to some extent. However, they ignore issues such as differences in multi-modality information, loss of spatial information, and under-utilization of boundary information, thereby limiting the further improvement of segmentation accuracy. This paper proposes a multimodal spatial information enhancement and boundary shape correction method consisting of a modality information extraction (MIE) module, a spatial information enhancement (SIE) module, and a boundary shape correction (BSC) module. The above three modules act on the input, backbone, and loss functions of deep convolutional networks (DCNN), respectively, and compose an end -to -end 3D brain tumor segmentation model. The three proposed modules can solve the low utilization rate of effective modality information, the insufficient spatial information acquisition ability, and the improper segmentation of key boundary positions can be solved. The proposed method was validated on BraTS2017, 2018, and 2019 datasets. Comparative experimental results confirmed the effectiveness and superiority of the proposed method over state-of-the-art segmentation methods.

基金机构：National Natural Science Foundation of China [62176081, U23A20294]; Chongqing talent group Project [cstc2024ycjh-bgzxm0156]; Science and Technology Innovation Key R&D Program of Chongqing [CSTB2023TIAD-409STX0016, CSTB2023TIAD-KPX0088, CSTB2022TIAD-KPX0039]

基金资助正文：This work is jointly supported by the National Natural Science Foundation of China under Grant 62176081 and U23A20294; Chongqing talent group Project:cstc2024ycjh-bgzxm0156; Science and Technology Innovation Key R&D Program of Chongqing CSTB2023TIAD-409STX0016, CSTB2023TIAD-KPX0088, CSTB2022TIAD-KPX0039.