Automatic Identification of Anatomical Locations for Bone Abnormalities in CT Imaging: A Multiplanar YOLOv5 Detection Approach

Abstract

Various deep learning applications have been developed to aid in the detection of bone abnormalities, such as tumors or osteolytic lesions. We propose a method for the automatic detection and classification of bones in CT scans to determine the anatomical location of these abnormalities. Our approach utilizes three 2D YOLOv5l models to predict the location and anatomical name of bones across the axial, coronal, and sagittal planes. We benchmarked this method against nnU-Net, a widely used 3D segmentation pipeline, using the TotalSegmentator dataset with synthetically generated osteolytic lesions and the RibFrac dataset, which contains annotated rib fractures. Our results show that our method outperforms nnU-Net in identifying lesions that are not located in the vertebrae or ribs, while nnU-Net excels in vertebra-level and rib-fracture localization. However, when predicting a range of possible rib or vertebra levels, rather than the exact levels, our method demonstrates highly accurate performance, outperforming nnU-Net. Overall, depending on the specific application, our work highlights that this multiplanar bone detection approach is a competitive alternative to 3D segmentation models for identifying bone abnormalities in CT scans.Clinical Relevance-In this paper, we have presented a reliable method for the localization and identification of bone tissue in CT scans to automatically provide anatomical information on bone abnormalities, streamlining reporting, and providing a framework for research and epidemiological studies.

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