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25 April 2024 |
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Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images | | Haomin Chen
; Yirui Wang
; Kang Zheng
; Weijian Li
; Chi-Tung Cheng
; Adam P. Harrison
; Jing Xiao
; Gregory D. Hager
; Le Lu
; Chien-Hung Liao
; Shun Miao
; | | Date: |
3 Jul 2020 | | Abstract: | Visual cues of enforcing bilaterally symmetric anatomies as normal findings
are widely used in clinical practice to disambiguate subtle abnormalities from
medical images. So far, inadequate research attention has been received on
effectively emulating this practice in CAD methods. In this work, we exploit
semantic anatomical symmetry or asymmetry analysis in a complex CAD scenario,
i.e., anterior pelvic fracture detection in trauma PXRs, where semantically
pathological (refer to as fracture) and non-pathological (e.g., pose)
asymmetries both occur. Visually subtle yet pathologically critical fracture
sites can be missed even by experienced clinicians, when limited diagnosis time
is permitted in emergency care. We propose a novel fracture detection framework
that builds upon a Siamese network enhanced with a spatial transformer layer to
holistically analyze symmetric image features. Image features are spatially
formatted to encode bilaterally symmetric anatomies. A new contrastive feature
learning component in our Siamese network is designed to optimize the deep
image features being more salient corresponding to the underlying semantic
asymmetries (caused by pelvic fracture occurrences). Our proposed method have
been extensively evaluated on 2,359 PXRs from unique patients (the largest
study to-date), and report an area under ROC curve score of 0.9771. This is the
highest among state-of-the-art fracture detection methods, with improved
clinical indications. | | Source: | arXiv, 2007.1464 | | Services: | Forum | Review | PDF | Favorites | |
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