HPCSeg-Net: Hippocampus Segmentation Network Integrating Autofocus Attention Mechanism and Feature Recombination and Recalibration Module

Liu, Bin; Zheng, Qian; Zhao, Kun; Li, Honglun; Ma, Chaoqing; Wu, Shan; Tong, Xiangrong

doi:10.1007/978-3-030-87358-5_63

Cited by 2 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inspired by [27,28], the MSCA-Module in Figure 3 was devised to be aware of deformations with distinct complexities by combining a trunk branch, a mask branch, and a feature source-shared skip connection. The trunk branch was to identify deformations with distinct complexities.…”

Section: 12mentioning

confidence: 99%

MSCAReg‐Net: Multi‐scale complexity‐aware convolutional neural network for deformable image registration

Yu,

Zheng,

et al. 2023

IET Image Processing

Self Cite

View full text Add to dashboard Cite

Deep learning‐based image registration (DLIR) has been widely developed, but it remains challenging in perceiving small and large deformations. Besides, the effectiveness of the DLIR methods was also rarely validated on the downstream tasks. In the study, a multi‐scale complexity‐aware registration network (MSCAReg‐Net) was proposed by devising a complexity‐aware technique to facilitate DLIR under a single‐resolution framework. Specifically, the complexity‐aware technique devised a multi‐scale complexity‐aware module (MSCA‐Module) to perceive deformations with distinct complexities, and employed a feature calibration module (FC‐Module) and a feature aggregation module (FA‐Module) to facilitate the MSCA‐Module by generating more distinguishable deformation features. Experimental results demonstrated the superiority of the proposed MSCAReg‐Net over the existing methods in terms of registration accuracy. Besides, other than the indices of Dice similarity coefficient (DSC) and percentage of voxels with non‐positive Jacobian determinant (), a comprehensive evaluation of the registration performance was performed by applying this method on a downstream task of multi‐atlas hippocampus segmentation (MAHS). Experimental results demonstrated that this method contributed to a better hippocampus segmentation over other DLIR methods, and a comparable segmentation performance with the leading SyN method. The comprehensive assessment including DSC, , and the downstream application on MAHS demonstrated the advances of this method.

show abstract

Section: 12mentioning

confidence: 99%