Tversky loss function for image segmentation using 3D fully convolutional deep networks

Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved F2 score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks.

READ FULL TEXT

page 3

page 6

page 7

research
03/28/2018

Tversky as a Loss Function for Highly Unbalanced Image Segmentation using 3D Fully Convolutional Deep Networks

Fully convolutional deep neural networks have been asserted to be fast a...
research
10/18/2018

A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

We propose a generalized focal loss function based on the Tversky index ...
research
09/23/2019

Class-dependent Compression of Deep Neural Networks

Today's deep neural networks require substantial computation resources f...
research
12/25/2017

Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss

As a basic task in computer vision, semantic segmentation can provide fu...
research
04/17/2021

Learning Fuzzy Clustering for SPECT/CT Segmentation via Convolutional Neural Networks

Quantitative bone single-photon emission computed tomography (QBSPECT) h...
research
10/26/2019

A Soft STAPLE Algorithm Combined with Anatomical Knowledge

Supervised machine learning algorithms, especially in the medical domain...

Please sign up or login with your details

Forgot password? Click here to reset