Volumetric segmentation of muscle compartments using in vivo imaging and architectural validation in human finger flexors
원문 초록
Segmenting muscle compartments and measuring their architecture can facilitate movement function assessment, accurate musculoskeletal modeling, and synergy-based electromyogram simulation. Here, we presented a novel method for volumetric segmentation of muscle compartments using in vivo imaging, focusing on the independent compartments for finger control of flexor digitorum superficialis (FDS). Besides, we measured the architectural properties of FDS compartments and validated the segmentation. Specifically, ultrasound and magnetic resonance imaging (MRI) from 10 healthy subjects were used for segmentation and measurement, while electromyography was utilized for validation. A two-step piecewise segmentation was proposed, first annotating compartment regions in the cross-sectional ultrasound image based on compartment movement, and then performing minimum energy matching to register the ultrasound data to the three-dimensional MRI coordinate system. Additionally, the architectural properties were measured in the compartment masks from the segmentation using MRI tractography. Anatomical correctness was verified by comparing known anatomy with reconstructed fiber tracts and measured properties, while segmentation accuracy was quantified as the percentage of finger electromyogram centers falling within their corresponding compartments. Results demonstrated agreement for the fiber orientation between the tractography and cadaveric photographs. Significant differences in architectural properties (P < 0.001) were observed between compartments. The properties of FDS and its compartments were within the physiological ranges (P < 0.01). 95% (38/40) of the electromyogram centers were located within respective compartments, with 2 errors occurring in the index and little fingers. The validated segmentation method and derived architectural properties may advance biomedical applications.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
PSAT: Pediatric Segmentation Approaches via Adult Augmentations and Transfer Learning
CF-Seg: Counterfactuals meet Segmentation
DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling
BOTM: Echocardiography Segmentation via Bi-directional Optimal Token Matching
TAGS: 3D Tumor-Adaptive Guidance for SAM
Alleviating Textual Reliance in Medical Language-guided Segmentation via Prototype-driven Semantic Approximation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.