Hippocampus Image Segmentation Based on Multi-view Fusion and 2.5D U-Net

NAVIGATE

Table of Contents

STATISTICS

Viewed31

Downloads21

Hippocampus Image Segmentation Based on Multi-view Fusion and 2.5D U-Net

[HTML] PDF下载 (21)

[1]CHEN Liwei,PENG Yifei,YU Renping,et al.Hippocampus Image Segmentation Based on Multi-view Fusion and 2.5D U-Net[J].Journal of Zhengzhou University (Engineering Science),2025,46(05):26-34.[doi:10.13705/j.issn.1671-6833.2025.02.013]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 46 Number of periods: 2025 05 Page number: 26-34 Column: Public date: 2025-08-10

Title:: Hippocampus Image Segmentation Based on Multi-view Fusion and 2.5D U-Net

Author(s):: CHEN Liwei; PENG Yifei; YU Renping; SUN Yuancheng; School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China

Keywords:: hippocampus image segmentation; CNN; U-Net; Triplet Attention; attention mechanism; volume fusion network

CLC:: TP391.7 TP183

DOI:: 10.13705/j.issn.1671-6833.2025.02.013

Abstract:: Aiming at the problem in existing methods of automatic segmentation of hippocampus image, which can not make good use of the context information, might lead to the difficulty in improving the segmentation accuracy and large memory consumption in the process of training and detection, a new model called MVF-2.5D U-Net based on multi-view fusion and 2.5D U-Net was introduced. Firstly, this model improved the 2D U-Net by incorporating a Triplet Attention module and adjusting the depth of the network. Secondly, the traditional single-slice input was replaced by a three-channel 2.5D image composed of adjacent slices. Finally, a volume fusion network was constructed to replace the conventional majority voting scheme. This study was validated by cross-validation on the HarP dataset. The experimental results showed that the average Dice coefficient and Hausdorff distance of the model on the hippocampus image segmentation task were 0.902 and 3.02, respectively, the accuracy and stability was better than the traditional U-Net model and comparison algorithm, and it was also suitable for the resource-constrained situation, which proved that the proposed model could achieve hippocampus segmentation on MRI more effectively.

References:: [1]SÄMANN P G, IGLESIAS J E, GUTMAN B, et al. FreeSurfer-based segmentation of hippocampal subfields: a review of methods and applications, with a novel quality control procedure for ENIGMA studies and other collaborative efforts[J]. Human Brain Mapping, 2022, 43(1): 207-233.
[2]JOULES R, WOLZ R. Fully automatic deep-learning based segmentation of hippocampal subfields from standard resolution T1W MRI in Alzheimer′s disease[J]. Alzheimer′s & Dementia, 2023, 19: e076575.
[3]SACKL M, TINAUER C, URSCHLER M, et al. Fully automated hippocampus segmentation using T2-informed deep convolutional neural networks[J]. NeuroImage, 2024, 298: 120767.
[4]NI T W, XIE L X, ZHENG H J, et al. Elastic boundary projection for 3D medical image segmentation[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 2109-2118.
[5]LIU M H, LI F, YAN H, et al. A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer′s disease[J]. NeuroImage, 2020, 208: 116459.
[6]ÇIÇEK Ö, ABDULKADIR A, LIENKAMP S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation[C]∥Medical Image Computing and Computer-Assisted Intervention. Cham:Springer, 2016: 424-432.
[7]MANJON J V, ROMERO J E, COUPE P. A novel deep learning based hippocampus subfield segmentation method [J]. Scientific Reports, 2022, 12(1): 1333.
[8]KNUTH F, ADDE I A, HUYNH B N, et al. MRI-based automatic segmentation of rectal cancer using 2D U-Net on two independent cohorts[J]. Acta Oncologica, 2022, 61(2): 255-263.
[9]ZETTLER N, MASTMEYER A. Comparison of 2D vs. 3D U-Net organ segmentation in abdominal 3D CT images [EB/OL].(2021-07-08)[2024-07-16].https:∥doi. org/10.48550/arXiv.2107.04062.
[10] ROTH H R, LU L, SEFF A, et al. A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations[C]∥ Medical Image Computing and Computer Assisted Intervention. Cham: Springer International Publishing, 2014: 520-527.
[11] ROTH H R, LU L, FARAG A, et al. DeepOrgan: multilevel deep convolutional networks for automated pancreas segmentation[C]∥Medical Image Computing and Computer Assisted Intervention. Cham: Springer, 2015: 556-564.
[12] YU Q H, XIE L X, WANG Y, et al. Recurrent saliency transformation network: incorporating multi-stage visual cues for small organ segmentation[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2018: 8280-8289.
[13] CHEN J, LU Y, YU Q, et al. TransUNet: Transformers make strong encoders for medical image segmentation [EB/OL].(2021-02-08)[2024-07-16].https:∥doi. org/10.48550/arXiv.2102.04306.
[14]欧宇轩, 高敏, 赵地, 等. SA-TF-UNet: 基于空间注意力机制和Transformer的MRI海马体分割[J]. 中国图象图形学报, 2023, 28(10): 3191-3202.
OU Y X, GAO M, ZHAO D, et al. SA-TF-UNet: a Transformer and spatial attention mechanisms based hippocampus segmentation network[J]. Journal of Image and Graphics, 2023, 28(10): 3191-3202.
[15] OKTAY O, SCHLEMPER J, LE FOLGOC L, et al. Attention U-Net: learning where to look for the pancreas [EB/OL].(2018-04-18) [2024-07-16] . https:∥ doi.org/10.48550/arXiv.1804.03999.
[16]WANG H, LEI C, ZHAO D, et al. DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism[J]. BMC Medical Imaging, 2023, 23(1): 158.
[17]魏明军, 王镆涵, 刘亚志, 等. 基于特征融合和混合注意力的小目标检测[J]. 郑州大学学报(工学版), 2024, 45(3): 72-79.
WEI M J, WANG M H, LIU Y Z, et al. Small object detection based on feature fusion and mixed attention[J]. Journal of Zhengzhou University (Engineering Science), 2024, 45(3): 72-79.
[18]高宇飞, 马自行, 徐静, 等. 基于卷积和可变形注意力的脑胶质瘤图像分割[J]. 郑州大学学报(工学版), 2024, 45(2): 27-32.
GAO Y F, MA Z X, XU J, et al. Brain glioma image segmentation based on convolution and deformable attention[J]. Journal of Zhengzhou University (Engineering Science), 2024, 45(2): 27-32.
[19] ZHOU Y Y, XIE L X, SHEN W, et al. A fixed-point model for pancreas segmentation in abdominal CT scans [C]∥Medical Image Computing and Computer Assisted Intervention. Cham: Springer, 2017: 693-701.
[20] XIA Y D, XIE L X, LIU F Z, et al. Bridging the gap between 2D and 3D organ segmentation with Volumetric Fusion Net[C]∥Medical Image Computing and Computer Assisted Intervention. Cham: Springer, 2018: 445-453.
[21] BOCCARDI M, BOCCHETTA M, MORENCY F C, et al. Training labels for hippocampal segmentation based on the EADC-ADNI harmonized hippocampal protocol[J]. Alzheimer′s & Dementia,2015, 11(2): 175-183.
[22] AVESTA A, HOSSAIN S, LIN M D, et al. Comparing 3D, 2.5D, and 2D approaches to brain image auto-segmentation[J]. Bioengineering, 2023, 10(2): 181.
[23] ATALOGLOU D, DIMOU A, ZARPALAS D, et al. Fast and precise hippocampus segmentation through deep convolutional neural network ensembles and transfer learning[J]. Neuroinformatics, 2019, 17(4): 563-582.
[24] CARMO D, SILVA B, INITIATIVE A D N, et al. Hippocampus segmentation on epilepsy and Alzheimer′s disease studies with multiple convolutional neural networks[J]. Heliyon, 2021, 7(2): e06226.
[25] NOBAKHT S, SCHAEFFER M, FORKERT N D, et al. Combined atlas and convolutional neural network-based segmentation of the hippocampus from MRI according to the ADNI harmonized protocol[J]. Sensors, 2021, 21 (7):2427.
[26] CHEN Y, YUE H L, KUANG H L, et al. RBS-Net: hippocampus segmentation using multi-layer feature learning with the region, boundary and structure loss[J]. Computers in Biology and Medicine, 2023, 160: 106953.
[27] ZHENG Q, LIU B, GAO Y, et al. HGM-cNet: integrating hippocampal gray matter probability map into a cascaded deep learning framework improves hippocampus segmentation[J]. European Journal of Radiology, 2023, 162: 110771.

Similar References:

Memo

Last Update: 2025-09-19