Tversky loss function for image segmentation using 3D fully convolutional deep networks
This work addresses data imbalance in medical imaging, particularly for lesion segmentation where false negatives are critical, though it is incremental as it builds on existing loss function modifications.
The paper tackled the problem of data imbalance in medical image segmentation by proposing a Tversky loss function for 3D fully convolutional networks, resulting in improved F2 score, Dice coefficient, and area under the precision-recall curve on multiple sclerosis lesion segmentation tasks.
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.