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Article Dans Une Revue International Journal of Computer Assisted Radiology and Surgery Année : 2022

Deep learning-based framework for segmentation of multiclass rib fractures in CT utilizing a multi-angle projection network

Résumé

PURPOSE: Clinical rib fracture diagnosis via computed tomography (CT) screening has attracted much attention in recent years. However, automated and accurate segmentation solutions remain a challenging task due to the large sets of 3D CT data to deal with. Down-sampling is often required to face computer constraints, but the performance of the segmentation may decrease in this case. METHODS: A new multi-angle projection network (MAPNet) method is proposed for accurately segmenting rib fractures by means of a deep learning approach. The proposed method incorporates multi-angle projection images to complementarily and comprehensively extract the rib characteristics using a rib extraction (RE) module and the fracture features using a fracture segmentation (FS) module. A multi-angle projection fusion (MPF) module is designed for fusing multi-angle spatial features. RESULTS: It is shown that MAPNet can capture more detailed rib fracture features than some commonly used segmentation networks. Our method achieves a better performance in accuracy (88.06 ± 6.97%), sensitivity (89.26 ± 5.69%), specificity (87.58% ± 7.66%) and in terms of classical criteria like dice (85.41 ± 3.35%), intersection over union (IoU, 80.37 ± 4.63%), and Hausdorff distance (HD, 4.34 ± 3.1). CONCLUSION: We propose a rib fracture segmentation technique to deal with the problem of automatic fracture diagnosis. The proposed method avoids the down-sampling of 3D CT data through a projection technique. Experimental results show that it has excellent potential for clinical applications.
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Dates et versions

hal-03691756 , version 1 (09-06-2022)

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Yuan Gao, Han Chen, Rongjun Ge, Zhan Wu, Hui Tang, et al.. Deep learning-based framework for segmentation of multiclass rib fractures in CT utilizing a multi-angle projection network. International Journal of Computer Assisted Radiology and Surgery, 2022, 17 (6), pp.1115-1124. ⟨10.1007/s11548-022-02607-1⟩. ⟨hal-03691756⟩
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