🤖 AI Summary
This study addresses the high cost of manual annotation in deep learning–based anatomical landmark segmentation by proposing a method grounded in statistical shape models (SSMs). Requiring only a single manual annotation, the approach generates thousands of high-quality synthetic 3D liver annotations suitable for training deep networks to segment the liver’s anterior edge and falciform ligament. The method substantially reduces annotation effort while achieving an average Intersection over Union (IoU) of 91.4% on 500 unseen synthetic samples. Qualitative evaluation on clinical data demonstrates strong generalization capability, suggesting the framework’s potential applicability to other anatomical structures beyond the liver.
📝 Abstract
Anatomical landmark segmentation serves as a critical initial step for robust multimodal registration during computer-assisted interventions. Current approaches predominantly rely on deep learning, which often necessitates the extensive manual generation of annotated datasets. In this paper, we present a novel strategy for creating large annotated datasets using a statistical shape model (SSM) based on a mean shape that is manually labeled only once. We demonstrate the method's efficacy through its application to deep-learning-based anatomical landmark segmentation, specifically targeting the detection of the anterior ridge and the falciform ligament in 3D liver shapes. A specialized deep learning network was trained with 8,800 annotated liver shapes generated by the SSM. The network's performance was evaluated on 500 unseen synthetic SSM shapes, yielding a mean Intersection over Union of 91.4% (87.4% for the anterior ridge and 87.6% for the falciform ligament). Subsequently, the network was applied to clinical patient liver shapes, with qualitative evaluation indicating promising results and highlighting the generalizability of the proposed approach. Our findings suggest that the SSM-based data generation approach alleviates the labor-intensive process of manual labeling while enabling the creation of large annotated training datasets for machine learning. Although our study focuses on liver anatomy, the proposed methodology holds potential for a broad range of applications where annotated training datasets play a pivotal role in developing accurate deep-learning models.