Segmentation of brain tumors from magnetic resonance imaging (MRI) data sets is of great importance for improved diagnosis, growth rate prediction, and treatment planning. However, automating this process is challenging due to the presence of severe partial volume effect and considerable variability in tumor structures, as well as imaging conditions, especially for the gliomas. In this paper, we introduce a new methodology that combines random forests and active contour model for the automated segmentation of the gliomas from multimodal volumetric MR images. Specifically, we employ a feature representations learning strategy to effectively explore both local and contextual information from multimodal images for tissue segmentation by using modality specific random forests as the feature learning kernels. Different levels of the structural information is subsequently integrated into concatenated and connected random forests for gliomas structure inferring. Finally, a novel multiscale patch driven active contour model is exploited to refine the inferred structure by taking advantage of sparse representation techniques. Results reported on public benchmarks reveal that our architecture achieves competitive accuracy compared to the state-of-the-art brain tumor segmentation methods while being computationally efficient.

The proposed method not only has application potential for cardiac diseases screening for large-scale CMR data, but also can be extended to other medical image research fields.

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3D left ventricle (LV) segmentation on echocardiography is very important for diagnosis and treatment of cardiac disease. It is not only because of that echocardiography is a real-time imaging technology and widespread in clinical application, but also because of that LV segmentation on 3D echocardiography can provide more full volume information of heart than LV segmentation on 2D echocardiography. However, 3D LV segmentation on echocardiography is still an open and challenging task owing to the lower contrast, higher noise and data dimensionality, limited annotation of 3D echocardiography. In this paper, we proposed a novel real-time framework, i.e., VoxelAtlasGAN, for 3D LV segmentation on 3D echocardiography. This framework has three contributions: 1) It is based on voxel-to-voxel conditional generative adversarial nets (cGAN). For the first time, cGAN is used for 3D LV segmentation on echocardiography. And cGAN advantageously fuses substantial 3D spatial context information from 3D echocardiography by self-learning structured loss; 2) For the first time, it embeds the atlas into an end-to-end optimization framework, which uses 3D LV atlas as a powerful prior knowledge to improve the inference speed, address the lower contrast and the limited annotation problems of 3D echocardiography; 3) It combines traditional discrimination loss and the new proposed consistent constraint, which further improves the generalization of the proposed framework. Voxe-lAtlasGAN was validated on 60 subjects on 3D echocardiography and it achieved satisfactory segmentation results and high inference speed. The mean surface distance is 1.85 mm, the mean hausdorff surface distance is 7.26 mm, mean dice is 0.953, the correlation of EF is 0.918, and the mean inference speed is 0.1s. These results have demonstrated that our proposed method has great potential for clinical application.