Multi-label Whole Heart Segmentation Using CNNs and Anatomical Label Configurations. Payer, C., Štern, D., Bischof, H., & Urschler, M. Volume 10663 LNCS. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 190-198. Springer, Cham, 2018. ![Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex We propose a pipeline of two fully convolutional networks for automatic multi-label whole heart segmentation from CT and MRI volumes. At first, a convolutional neural network (CNN) localizes the center of the bounding box around all heart structures, such that the subsequent segmentation CNN can focus on this region. Trained in an end-to-end manner, the segmentation CNN transforms intermediate label predictions to positions of other labels. Thus, the network learns from the relative positions among labels and focuses on anatomically feasible configurations. Results on the MICCAI 2017 Multi-Modality Whole Heart Segmentation (MM-WHS) challenge show that the proposed architecture performs well on the provided CT and MRI training volumes, delivering in a three-fold cross validation an average Dice Similarity Coefficient over all heart substructures of 88.9% and 79.0%, respectively. Moreover, on the MM-WHS challenge test data we rank first for CT and second for MRI with a whole heart segmentation Dice score of 90.8% and 87%, respectively, leading to an overall first ranking among all participants.
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abstract = {We propose a pipeline of two fully convolutional networks for automatic multi-label whole heart segmentation from CT and MRI volumes. At first, a convolutional neural network (CNN) localizes the center of the bounding box around all heart structures, such that the subsequent segmentation CNN can focus on this region. Trained in an end-to-end manner, the segmentation CNN transforms intermediate label predictions to positions of other labels. Thus, the network learns from the relative positions among labels and focuses on anatomically feasible configurations. Results on the MICCAI 2017 Multi-Modality Whole Heart Segmentation (MM-WHS) challenge show that the proposed architecture performs well on the provided CT and MRI training volumes, delivering in a three-fold cross validation an average Dice Similarity Coefficient over all heart substructures of 88.9% and 79.0%, respectively. Moreover, on the MM-WHS challenge test data we rank first for CT and second for MRI with a whole heart segmentation Dice score of 90.8% and 87%, respectively, leading to an overall first ranking among all participants.},
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chapter = {Multi-label Whole Heart Segmentation Using CNNs and Anatomical Label Configurations},
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