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

## TL;DR

This paper introduces a Tversky loss function for 3D deep neural networks to better handle data imbalance in medical image segmentation, improving sensitivity and overall performance.

## Contribution

It proposes a generalized Tversky loss function that enhances training of 3D fully convolutional networks for imbalanced medical image segmentation tasks.

## Key findings

- Improved F2 score, Dice coefficient, and precision-recall area under curve.
- Better trade-off between precision and recall in lesion segmentation.
- Effective handling of data imbalance in medical imaging.

## Abstract

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.