ArXiv Preprint
Myocardial pathology segmentation (MyoPS) is critical for the risk
stratification and treatment planning of myocardial infarction (MI).
Multi-sequence cardiac magnetic resonance (MS-CMR) images can provide valuable
information. For instance, balanced steady-state free precession cine sequences
present clear anatomical boundaries, while late gadolinium enhancement and
T2-weighted CMR sequences visualize myocardial scar and edema of MI,
respectively. Existing methods usually fuse anatomical and pathological
information from different CMR sequences for MyoPS, but assume that these
images have been spatially aligned. However, MS-CMR images are usually
unaligned due to the respiratory motions in clinical practices, which poses
additional challenges for MyoPS. This work presents an automatic MyoPS
framework for unaligned MS-CMR images. Specifically, we design a combined
computing model for simultaneous image registration and information fusion,
which aggregates multi-sequence features into a common space to extract
anatomical structures (i.e., myocardium). Consequently, we can highlight the
informative regions in the common space via the extracted myocardium to improve
MyoPS performance, considering the spatial relationship between myocardial
pathologies and myocardium. Experiments on a private MS-CMR dataset and a
public dataset from the MYOPS2020 challenge show that our framework could
achieve promising performance for fully automatic MyoPS.
Wangbin Ding, Lei Li, Junyi Qiu, Sihan Wang, Liqin Huang, Yinyin Chen, Shan Yang, Xiahai Zhuang
2023-02-07
