A wrapper method for finding optimal subset of multimodal Magnetic Resonance Imaging sequences for ischemic stroke lesion segmentation.

Multimodal data, while being information-rich, contains complementary as well as redundant information. Depending on the target problem some modalities are more informative and thus relevant for decision-making. Identifying the optimal subset of modalities best suited to solve a particular task significantly reduces the complexity of acquisition without compromising performance. In this work, we propose a wrapper method for examining the importance of Magnetic Resonance Imaging (MRI) sequences for ischemic stroke lesion segmentation using a deep neural network trained for segmentation. Importance score for each modality is computed through a combinatorial dropout of input modalities at inference coupled with a systematic evaluation its impact on the model's performance. Experimental evaluation of the proposed method is performed on two publicly available datasets: (i) ISLES15 - comprising seven MRI sequences for 30 cases and (ii) ISLES22 - comprising of three MRI sequences for 250 cases. We identified DWI, Tmax and T1c as the optimal set of MRI sequences for core-penumbra delineation and Tmax as the optimal sequence for lesion segmentation in ISLES15 dataset. In ISLES22 dataset, DWI was identified as the optimal sequence for lesion segmentation. In addition to the exhaustive experimental validation, visually interpretable evidence for accuracy of the identified optimal subset is provided in the form of saliency maps.