# Segmentation of Head and Neck Organs at Risk Using CNN with Batch Dice Loss

arXiv: https://arxiv.org/abs/1812.02427 · HTML: https://mlsanity.com/paper/1812.02427/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation](https://mlsanity.com/paper/2205.11096/) (2022)
- [Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentation](https://mlsanity.com/paper/2201.01636/) (2022)
- [The Dice loss in the context of missing or empty labels: Introducing Phi and ε](https://mlsanity.com/paper/2207.09521/) (2022)
- [Partial annotations for the segmentation of large structures with low annotation cost](https://mlsanity.com/paper/2209.12216/) (2022)
- [Contour Dice loss for structures with Fuzzy and Complex Boundaries in Fetal MRI](https://mlsanity.com/paper/2209.12232/) (2022)

## Abstract (arXiv)

This paper deals with segmentation of organs at risk (OAR) in head and neck area in CT images which is a crucial step for reliable intensity modulated radiotherapy treatment. We introduce a convolution neural network with encoder-decoder architecture and a new loss function, the batch soft Dice loss function, used to train the network. The resulting model produces segmentations of every OAR in the public MICCAI 2015 Head And Neck Auto-Segmentation Challenge dataset. Despite the heavy class imbalance in the data, we improve accuracy of current state-of-the-art methods by 0.33 mm in terms of average surface distance and by 0.11 in terms of Dice overlap coefficient on average.