V-Net：Fully Convolutional Neural Networks
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V-Net: Fully Convolutional Neural Networks for
Volumetric Medical Image Segmentation
Fausto Milletari
1
, Nassir Navab
1,2
, Seyed-Ahmad Ahmadi
3
1
Computer Aided Medical Procedures, Technische Universit¨at M¨unchen, Germany
2
Computer Aided Medical Procedures, Johns Hopkins University, Baltimore, USA
3
Department of Neurology, Klinikum Grosshadern, Ludwig-Maximilians-Universit¨at
M¨unchen, Germany
Abstract. Convolutional Neural Networks (CNNs) have been recently
employed to solve problems from both the computer vision and medi-
cal image analysis fields. Despite their popularity, most approaches are
only able to process 2D images while most medical data used in clinical
practice consists of 3D volumes. In this work we propose an approach
to 3D image segmentation based on a volumetric, fully convolutional,
neural network. Our CNN is trained end-to-end on MRI volumes depict-
ing prostate, and learns to predict segmentation for the whole volume at
once. We introduce a novel objective function, that we optimise during
training, based on Dice coefficient. In this way we can deal with situa-
tions where there is a strong imbalance between the number of foreground
and background voxels. To cope with the limited number of annotated
volumes available for training, we augment the data applying random
non-linear transformations and histogram matching. We show in our ex-
perimental evaluation that our approach achieves good performances on
challenging test data while requiring only a fraction of the processing
time needed by other previous methods.
1 Introduction and Related Work
Recent research in computer vision and pattern recognition has highlighted the
capabilities of Convolutional Neural Networks (CNNs) to solve challenging tasks
such as classification, segmentation and object detection, achieving state-of-the-
art performances. This success has been attributed to the ability of CNNs to
learn a hierarchical representation of raw input data, without relying on hand-
crafted features. As the inputs are processed through the network layers, the
level of abstraction of the resulting features increases. Shallower layers grasp
local information while deeper layers use filters whose receptive fields are much
broader that therefore capture global information [19].
Segmentation is a highly relevant task in medical image analysis. Automatic
delineation of organs and structures of interest is often necessary to perform tasks
such as visual augmentation [10], computer assisted diagnosis [12], interventions
[20] and extraction of quantitative indices from images [1]. In particular, since
diagnostic and interventional imagery often consists of 3D images, being able to
arXiv:1606.04797v1 [cs.CV] 15 Jun 2016
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