# FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images

arXiv: https://arxiv.org/abs/1907.12056 · HTML: https://mlsanity.com/paper/1907.12056/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2019.

## Built on by (most cited first, 24 of 24)

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- [SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma](https://mlsanity.com/paper/2312.09576/) (2023)
- [Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification](https://mlsanity.com/paper/2406.05596/) (2024)
- [FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images](https://mlsanity.com/paper/2104.01771/) (2021)
- [Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training](https://mlsanity.com/paper/2103.16493/) (2021)
- [Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss](https://mlsanity.com/paper/2102.01897/) (2021)
- [A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images](https://mlsanity.com/paper/2109.12634/) (2021)
- [Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentation](https://mlsanity.com/paper/2201.01636/) (2022)
- [Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation](https://mlsanity.com/paper/2303.00232/) (2023)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentation](https://mlsanity.com/paper/2407.12611/) (2024)
- [The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review](https://mlsanity.com/paper/2505.06118/) (2025)
- [Comparing Bayesian Models for Organ Contouring in Head and Neck Radiotherapy](https://mlsanity.com/paper/2111.01134/) (2021)
- [HMRNet: High and Multi-Resolution Network with Bidirectional Feature Calibration for Brain Structure Segmentation in Radiotherapy](https://mlsanity.com/paper/2206.02959/) (2022)
- [Deep-learning Segmentation of Small Volumes in CT images for Radiotherapy Treatment Planning](https://mlsanity.com/paper/2404.04202/) (2024)
- [Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation](https://mlsanity.com/paper/2501.09116/) (2025)
- [Nested-block self-attention for robust radiotherapy planning segmentation](https://mlsanity.com/paper/2102.13541/) (2021)
- [Dual Shape Guided Segmentation Network for Organs-at-Risk in Head and Neck CT Images](https://mlsanity.com/paper/2110.12192/) (2021)
- [Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning](https://mlsanity.com/paper/2504.18582/) (2025)

## Abstract (arXiv)

In this paper, we propose an end-to-end deep neural network for solving the problem of imbalanced large and small organ segmentation in head and neck (HaN) CT images. To conduct radiotherapy planning for nasopharyngeal cancer, more than 10 organs-at-risk (normal organs) need to be precisely segmented in advance. However, the size ratio between large and small organs in the head could reach hundreds. Directly using such imbalanced organ annotations to train deep neural networks generally leads to inaccurate small-organ label maps. We propose a novel end-to-end deep neural network to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs with specifically designed small-organ sub-networks while maintaining the accuracy of large organ segmentation. A strong main network with densely connected atrous spatial pyramid pooling and squeeze-and-excitation modules is used for segmenting large organs, where large organs' label maps are directly output. For small organs, their probabilistic locations instead of label maps are estimated by the main network. High-resolution and multi-scale feature volumes for each small organ are ROI-pooled according to their locations and are fed into small-organ networks for accurate segmenting small organs. Our proposed network is extensively tested on both collected real data and the MICCAI Head and Neck Auto Segmentation Challenge 2015 dataset, and shows superior performance compared with state-of-the-art segmentation methods.