# Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentation

arXiv: https://arxiv.org/abs/2201.01636 · HTML: https://mlsanity.com/paper/2201.01636/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 5 Jan 2022.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation](https://mlsanity.com/paper/1705.08302/) (2017)
- [Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy](https://mlsanity.com/paper/1809.04430/) (2018)
- [AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy](https://mlsanity.com/paper/1808.05238/) (2018)
- [Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation](https://mlsanity.com/paper/2102.10365/) (2021)
- [FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images](https://mlsanity.com/paper/1907.12056/) (2019)
- [Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search](https://mlsanity.com/paper/2004.08426/) (2020)
- [Hierarchical 3D fully convolutional networks for multi-organ segmentation](https://mlsanity.com/paper/1704.06382/) (2017)
- [FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images](https://mlsanity.com/paper/2104.01771/) (2021)
- [Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation](https://mlsanity.com/paper/2012.09279/) (2020)
- [A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images](https://mlsanity.com/paper/2109.12634/) (2021)
- [Segmentation of Head and Neck Organs at Risk Using CNN with Batch Dice Loss](https://mlsanity.com/paper/1812.02427/) (2018)

## Built on by (most cited first, 6 of 6)

- [Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation](https://mlsanity.com/paper/2303.00232/) (2023)
- [Fully Automatic Segmentation of Gross Target Volume and Organs-at-Risk for Radiotherapy Planning of Nasopharyngeal Carcinoma](https://mlsanity.com/paper/2310.02972/) (2023)
- [MOSAIC: A Multi-View 2.5D Organ Slice Selector with Cross-Attentional Reasoning for Anatomically-Aware CT Localization in Medical Organ Segmentation](https://mlsanity.com/paper/2505.10672/) (2025)
- [Alleviating Class Imbalance in Semi-supervised Multi-organ Segmentation via Balanced Subclass Regularization](https://mlsanity.com/paper/2408.14047/) (2024)
- [EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI](https://mlsanity.com/paper/2607.03568/) (2026)
- [Importance-Aware Feature Sparsification for Wireless Split Learning](https://mlsanity.com/paper/2609.39194/) (2026)

## Abstract (arXiv)

The segmentation of organs at risk (OAR) is a required precondition for the cancer treatment with image guided radiation therapy. The automation of the segmentation task is therefore of high clinical relevance. Deep Learning (DL) based medical image segmentation is currently the most successful approach, but suffers from the over-presence of the background class and the anatomically given organ size difference, which is most severe in the head and neck (HAN) area. To tackle the HAN area specific class imbalance problem we first optimize the patch-size of the currently best performing general purpose segmentation framework, the nnU-Net, based on the introduced class imbalance measurement, and second, introduce the class adaptive Dice loss to further compensate for the highly imbalanced setting. Both the patch-size and the loss function are parameters with direct influence on the class imbalance and their optimization leads to a 3% increase of the Dice score and 22% reduction of the 95% Hausdorff distance compared to the baseline, finally reaching 0.8±0.15 and 3.17±1.7 mm for the segmentation of seven HAN organs using a single and simple neural network. The patch-size optimization and the class adaptive Dice loss are both simply integrable in current DL based segmentation approaches and allow to increase the performance for class imbalanced segmentation tasks.