V-Net is a fully convolutional neural network designed for volumetric medical image segmentation. The paper introduces a novel approach to 3D image segmentation using a volumetric CNN trained end-to-end on MRI volumes of the prostate. The network uses a Dice coefficient-based objective function to optimize segmentation, which is particularly effective in handling imbalanced foreground and background voxel counts. To address the limited availability of annotated data, the authors apply random non-linear transformations and histogram matching for data augmentation. The network architecture consists of a compression path and a decompression path, with residual learning used to improve convergence and performance. The model is trained on 50 MRI volumes from the PROMISE2012 dataset and tested on 30 unseen volumes, achieving high accuracy and efficiency. The results show that V-Net outperforms previous methods in terms of Dice coefficient, Hausdorff distance, and overall segmentation quality. The model is implemented in Python using a custom Caffe framework and runs efficiently on standard hardware, segmenting new volumes in under a second. The paper concludes that V-Net provides a fast and accurate solution for 3D medical image segmentation, with potential for future applications in other modalities and higher resolutions.V-Net is a fully convolutional neural network designed for volumetric medical image segmentation. The paper introduces a novel approach to 3D image segmentation using a volumetric CNN trained end-to-end on MRI volumes of the prostate. The network uses a Dice coefficient-based objective function to optimize segmentation, which is particularly effective in handling imbalanced foreground and background voxel counts. To address the limited availability of annotated data, the authors apply random non-linear transformations and histogram matching for data augmentation. The network architecture consists of a compression path and a decompression path, with residual learning used to improve convergence and performance. The model is trained on 50 MRI volumes from the PROMISE2012 dataset and tested on 30 unseen volumes, achieving high accuracy and efficiency. The results show that V-Net outperforms previous methods in terms of Dice coefficient, Hausdorff distance, and overall segmentation quality. The model is implemented in Python using a custom Caffe framework and runs efficiently on standard hardware, segmenting new volumes in under a second. The paper concludes that V-Net provides a fast and accurate solution for 3D medical image segmentation, with potential for future applications in other modalities and higher resolutions.