Multi-sequence myocardium segmentation with cross-constrained shape and neural network-based initialization

Abstract

For myocardial infarction (MI) patients, delayed enhancement (DE) and T2-weighted cardiovascular magnetic resonance imaging (CMR) can play significant roles in diagnosis, prognosis and therapeutic strategy evaluation. However, the non-rigid registration between different CMR sequences is particularly challenging and prevents the use of multi-sequence image analysis. In this article, we propose an approach for segmenting T2 and DE CMR simultaneously with cross-constrained shape and shape discrepancy compensation. A framework for the unified segmentation of multi-sequence images is built based on the coupled level set method. Additionally, a sparse representation-based shape model is optimized under the constraints from both sequences for complementary information sharing. Considering the myocardium shape discrepancy between the two sequences due to non-perfect registration, an error term is added to explicitly model this difference. The intensity feature is extracted with a Gaussian mixture model from each sequence. To obtain a fully automatic approach, the conditional generative adversarial network is adopted for initialization. The results are evaluated with T2 and DE images from 32 MI patients. A promising Dice similarity coefficient of the myocardium is achieved (84.97±4.15% for T2 and 78.13±6.22% for DE CMR). This approach is a pilot work toward automatic, multi-sequence CMR image analysis.