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We present an efficient deep learning approach for the challenging task oftumor segmentation in multisequence MR images. In recent years, ConvolutionalNeural Networks (CNN) have achieved state-of-the-art performances in a largevariety of recognition tasks in medical imaging. Because of the considerablecomputational cost of CNNs, large volumes such as MRI are typically processedby subvolumes, for instance slices (axial, coronal, sagittal) or small 3Dpatches. In this paper we introduce a CNN-based model which efficientlycombines the advantages of the short-range 3D context and the long-range 2Dcontext. To overcome the limitations of specific choices of neural networkarchitectures, we also propose to merge outputs of several cascaded 2D-3Dmodels by a voxelwise voting strategy. Furthermore, we propose a networkarchitecture in which the different MR sequences are processed by separatesubnetworks in order to be more robust to the problem of missing MR sequences.Finally, a simple and efficient algorithm for training large CNN models isintroduced. We evaluate our method on the public benchmark of the BRATS 2017challenge on the task of multiclass segmentation of malignant brain tumors. Ourmethod achieves good performances and produces accurate segmentations withmedian Dice scores of 0.918 (whole tumor), 0.883 (tumor core) and 0.854(enhancing core). Our approach can be naturally applied to various tasksinvolving segmentation of lesions or organs.

3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context