Asymmetry Disentanglement Network for Interpretable Acute Ischemic
Stroke Infarct Segmentation in Non-Contrast CT Scans
- URL: http://arxiv.org/abs/2206.15445v1
- Date: Thu, 30 Jun 2022 17:39:28 GMT
- Title: Asymmetry Disentanglement Network for Interpretable Acute Ischemic
Stroke Infarct Segmentation in Non-Contrast CT Scans
- Authors: Haomiao Ni, Yuan Xue, Kelvin Wong, John Volpi, Stephen T.C. Wong,
James Z. Wang, Xiaolei Huang
- Abstract summary: We propose a novel Asymmetry Disentanglement Network (ADN) to automatically separate pathological asymmetries and intrinsic anatomical asymmetries in NCCTs.
ADN first performs asymmetry disentanglement based on input NCCTs, which produces different types of 3D asymmetry maps.
ADN achieves state-of-the-art AIS segmentation performance on a public NCCT dataset.
- Score: 16.901081820073593
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Accurate infarct segmentation in non-contrast CT (NCCT) images is a crucial
step toward computer-aided acute ischemic stroke (AIS) assessment. In clinical
practice, bilateral symmetric comparison of brain hemispheres is usually used
to locate pathological abnormalities. Recent research has explored asymmetries
to assist with AIS segmentation. However, most previous symmetry-based work
mixed different types of asymmetries when evaluating their contribution to AIS.
In this paper, we propose a novel Asymmetry Disentanglement Network (ADN) to
automatically separate pathological asymmetries and intrinsic anatomical
asymmetries in NCCTs for more effective and interpretable AIS segmentation. ADN
first performs asymmetry disentanglement based on input NCCTs, which produces
different types of 3D asymmetry maps. Then a synthetic,
intrinsic-asymmetry-compensated and pathology-asymmetry-salient NCCT volume is
generated and later used as input to a segmentation network. The training of
ADN incorporates domain knowledge and adopts a tissue-type aware regularization
loss function to encourage clinically-meaningful pathological asymmetry
extraction. Coupled with an unsupervised 3D transformation network, ADN
achieves state-of-the-art AIS segmentation performance on a public NCCT
dataset. In addition to the superior performance, we believe the learned
clinically-interpretable asymmetry maps can also provide insights towards a
better understanding of AIS assessment. Our code is available at
https://github.com/nihaomiao/MICCAI22_ADN.
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