We extensively evaluated our method on the data set of the multi-modality whole heart segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The dice values of whole heart segmentation are 0.914 (CT images) and 0.890 (MRI images), which are both higher than the state-of-the-art...
A whole heart segmentation (WHS) method is presented for cardiac MRI. This segmentation method employs multi-modality atlases from MRI and CT and adopts a new label fusion algorithm which is based on the proposed multi-scale patch (MSP) strategy and a new global atlas ranking scheme. MSP, de...
Despite these obstacles, the works demonstrate that combining modalities can improve performance over single-modal approaches for tasks like tumor segmentation, diagnosis, and survival prediction. 3.7. Ophthalmology This category includes studies that focus on utilizing multimodal data integration to examine ...
This method performed excellently in simultaneously labeling the seven sub-structures of the heart in the Multi-Modality Whole Heart Segmentation (MM-WHS) Challenge 2017 dataset. In the same year, a network consisting of two FCNs was proposed by Payer et al. for multi-label whole heart ...
This approach allows for fine-tuning the accuracy of micro-object segmentation by adapting the size of the segmented images. The efficacy of our method is rigorously validated on the Multi-Modality Whole Heart Segmentation (MM-WHS) Challenge 2017 dataset, demonstrating competitive results and the ...