The paper "V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation" by Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi introduces a novel approach to 3D image segmentation using fully convolutional neural networks (FCNNs). The authors address the challenge of processing 3D medical volumes, which are common in clinical practice, by proposing a volumetric, end-to-end trained CNN specifically designed for MRI prostate segmentation. They introduce a new objective function based on the Dice coefficient to handle imbalances between foreground and background voxels. To address the limited number of annotated volumes, they augment the training data with random non-linear transformations and histogram matching. The experimental results show that their method achieves good performance on challenging test data, requiring only a fraction of the processing time compared to previous methods. The network architecture is designed to learn residual functions, improving both convergence time and segmentation accuracy. The paper also includes a detailed description of the network architecture, training process, and evaluation metrics, demonstrating the effectiveness of the proposed approach in medical image segmentation tasks.The paper "V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation" by Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi introduces a novel approach to 3D image segmentation using fully convolutional neural networks (FCNNs). The authors address the challenge of processing 3D medical volumes, which are common in clinical practice, by proposing a volumetric, end-to-end trained CNN specifically designed for MRI prostate segmentation. They introduce a new objective function based on the Dice coefficient to handle imbalances between foreground and background voxels. To address the limited number of annotated volumes, they augment the training data with random non-linear transformations and histogram matching. The experimental results show that their method achieves good performance on challenging test data, requiring only a fraction of the processing time compared to previous methods. The network architecture is designed to learn residual functions, improving both convergence time and segmentation accuracy. The paper also includes a detailed description of the network architecture, training process, and evaluation metrics, demonstrating the effectiveness of the proposed approach in medical image segmentation tasks.