3D hippocampal segmentation based on spatial and frequency domain features adaptive fusion and inter⁃class boundary region enhancement
10.16098/j.issn.0529-1356.2024.01.011
- Author:
He BAI
1
;
Ye TENG
1
;
Lei FENG
1
;
Hai-Wei MENG
1
;
Yu-Chun TANG
1
;
Shu-Wei LIU
1
;
He BAI
2
;
Ye TENG
2
;
Lei FENG
2
;
Hai-Wei MENG
2
;
Yu-Chun TANG
2
;
Shu-Wei LIU
2
Author Information
1. Institute of Sectional Anatomy and Digital Human, Department of Anatomy and Neurobiology, School of Basic Medical Sciences, Shandong University
2. Institute of Brain and Brain-Inspired Sciences, Shandong University
- Publication Type:Journal Article
- Keywords:
Hippocampal segmentation;
Human;
Inter-class boundary region enhancement;
Medical image segmentation;
Spatial and frequency domain features adaptive fusion
- From:
Acta Anatomica Sinica
2024;55(1):73-81
- CountryChina
- Language:Chinese
-
Abstract:
Objective Hippocampal atrophy is a clinically important marker for the diagnosis of many psychiatric disorders such as Alzheimer’s disease‚ so accurate segmentation of the hippocampus is an important scientific issue. With the development of deep learning‚ a large number of advanced automatic segmentation method have been proposed. However‚ 3D hippocampal segmentation is still challenging due to the effects of various noises in MRI and unclear boundaries between various classes of the hippocampus. Therefore‚ the aim of this paper is to propose new method to segment the hippocampal head‚ body‚ and tail more accurately. Methods To overcome these challenges‚ this paper proposed two strategies. One was the spatial and frequency domain features adaptive fusion strategy‚ which reduced the influence of noise on feature extraction by automatically selecting the appropriate frequency combination through fast Fourier transform and convolution. The other was an inter-class boundary region enhancement strategy‚ which allowed the network to focus on learning the boundary regions by weighting the loss function of the boundary regions between each class to achieve the goal of pinpointing the boundaries and regulating the size of the hippocampal head‚ body and tail. Results Experiments performed on a 50-case teenager brain MRI dataset show that our method achieves state-of-the-art hippocampal segmentation. Hippocampal head‚ body and tail had been improved compared to the existing method. Ablation experiments demonstrated the effectiveness of our two proposed strategies‚ and we also validated that the network had a strong generalization ability on a 260-case Task04_Hippocampus dataset. It was shown that the method proposed in this paper could be used in more hippocampal segmentation scenarios. Conclusion The method proposed in this paper can help clinicians to observe hippocampal atrophy more clearly and accomplish more accurate diagnosis and follow-up of the condition.
