Segmentation of Head and Neck Organs at Risk Using CNN with Batch Dice Loss
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Dec 2018.
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Citing papers per year: 2022: 5.
5 papers.
- 2022FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentationcited by 12
- 2022Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentationcited by 6
- 2022The Dice loss in the context of missing or empty labels: Introducing Phi and εcited by 5
- 2022Partial annotations for the segmentation of large structures with low annotation costcited by 1
- 2022Contour Dice loss for structures with Fuzzy and Complex Boundaries in Fetal MRIcited by 1
- 2022Contour Dice loss for structures with Fuzzy and Complex Boundaries in Fetal MRI25 Sep 2022
- 2022Partial annotations for the segmentation of large structures with low annotation cost25 Sep 2022
- 2022The Dice loss in the context of missing or empty labels: Introducing Phi and ε19 Jul 2022
- 2022FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation23 May 2022
- 2022Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentation5 Jan 2022