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A Patient-Specific Model for Predicting Tibia Soft Tissue Insertions From Bony Outlines Using a Spatial Structure Supervised Learning Framework
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DOI：10.1109/THMS.2016.2545924.png)
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Recreating the natural anatomy in ligament reconstruction is crucial to fully restore the knee joint function and reduce impingement on iatrogenic injury to adjacent structures, yet is subject to the difficulties in locating ligament and other associated soft tissues insertion sites intraoperatively and the high interperson morphological variability cross patients. In this study, we present a new quantitative analysis method capable of achieving personalized identification of cruciate ligament and soft tissue insertions. We craft patient-specific features of tibia outline that can be accurately and reliably measured from CT images. In addition, we propose a supervised structure learning and prediction model with special interdimensional and response structure regularization terms to capture relationship between the spatial arrangement of soft tissue insertions and the patient-specific features extracted from the tibia outlines. In the experiment, the proposed model outperforms baseline models and provides an accurate and accessible approach that can be used as the first and the most critical step to achieve personalized surgical planning in cruciate ligament reconstruction.
Keyword:
Cruciate ligament and meniscal insertions
data mining
knee anatomic reconstruction
patient-specific prediction
structured supervised learning
X-ray imaging and computed tomography
期刊
IF:
4.4
论文数:
1.1K
被引数:
3.5K
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