3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation

We present a novel automated method to segment the myocardium of both left and right ventricles in MRI volumes. The segmentation is consistent in 3D across the slices such that it can be directly used for mesh generation. Two specific neural networks with multi-scale coarse-to-fine prediction structure are proposed to cope with the small training dataset and trained using an original loss function. The former segments a slice in the middle of the volume. Then the latter iteratively propagates the slice segmentations towards the base and the apex, in a spatially consistent way. We perform 5-fold cross-validation on the 15 cases from STACOM to validate the method. For training, we use real cases and their synthetic variants generated by combining motion simulation and image synthesis. Accurate and consistent testing results are obtained

Similarity enhancement for automatic segmentation of cardiac structures in computed tomography volumes.

International audienceThe aim of this research is proposing a 3-D similarity enhancement technique useful for improving the segmentation of cardiac structures in Multi-Slice Computerized Tomography (MSCT) volumes. The similarity enhancement is obtained by subtracting the intensity of the current voxel and the gray levels of their adjacent voxels in two volumes resulting after preprocessing. Such volumes are: a. - a volume obtained after applying a Gaussian distribution and a morphological top-hat filter to the input and b. - a smoothed volume generated by processing the input with an average filter. Then, the similarity volume is used as input to a region growing algorithm. This algorithm is applied to extract the shape of cardiac structures, such as left and right ventricles, in MSCT volumes. Qualitative and quantitative results show the good performance of the proposed approach for discrimination of cardiac cavities

Whole heart segmentation from CT images using 3D U-Net architecture

Recent studies have demonstrated the importance of neural networks in medical image processing and analysis. However, their great efficiency in segmentation tasks is highly dependent on the amount of training data. When these networks are used on small datasets, the process of data augmentation can be very significant. We propose a convolutional neural network approach for the whole heart segmentation which is based upon the 3D U-Net architecture and incorporates principle component analysis as an additional data augmentation technique. The network is trained end-to-end i.e. no pre-trained network is required. Evaluation of the proposed approach is performed on 20 3D CT images from MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, divided into 15 training and 5 validation images. Final segmentation results show a high Dice coefficient overlap to ground truth, indicating that the proposed approach is competitive to state-of-the-art. Additionally, we provide the discussion of the influence of different learning rates on the final segmentation results

Segmentation 3D multi-objets d'images scanner cardiaques : une approche multi-agents

International audienceNous proposons une nouvelle méthode de segmentation permettant une détection multi-objets, semi-interactive et à caractère générique, appliquée à l'extraction de structures cardiaques en imagerie scanner multibarettes. L'approche proposée repose sur l'élaboration d'un schéma multi-agents combiné à une méthode de classification supervisée qui permet l'introduction d'a priori dans le processus de segmentation ainsi que des temps de calcul rapides. Le système multi-agents proposé est centralisé autour d'un agent communiquant qui contrôle une population d'agents situés dans l'image dont le rôle est d'assurer la segmentation au moyen d'interactions de type coopératif et compétitif. La méthode proposée a été testée sur plusieurs bases de données patient. Quelques résultats représentatifs sont finalement présentés et discutés