mlsanityData to 14 Oct 2026

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

arXiv 1907.12056 ↗First posted 28 Jul 2019eess.IV cs.CVcited by 24

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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.

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

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Citing papers per year: 2021: 8; 2022: 5; 2023: 5; 2024: 3; 2025: 3.

24 papers.

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  1. 2022AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentationcited by 351
  2. 2021AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentationcited by 48
  3. 2022A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmarkcited by 45
  4. 2023Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspectivecited by 43
  5. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 33
  6. 2023Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentationcited by 33
  7. 2023SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinomacited by 29
  8. 2024Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classificationcited by 18
  9. 2021FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Imagescited by 17
  10. 2021Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Trainingcited by 10
  11. 2021Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Losscited by 9
  12. 2021A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Imagescited by 7
12 more
  1. 2022Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentationcited by 6
  2. 2023Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentationcited by 3
  3. 2023Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotationcited by 2
  4. 2024Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentationcited by 2
  5. 2025The Application of Deep Learning for Lymph Node Segmentation: A Systematic Reviewcited by 2
  6. 2021Comparing Bayesian Models for Organ Contouring in Head and Neck Radiotherapycited by 1
  7. 2022HMRNet: High and Multi-Resolution Network with Bidirectional Feature Calibration for Brain Structure Segmentation in Radiotherapycited by 1
  8. 2024Deep-learning Segmentation of Small Volumes in CT images for Radiotherapy Treatment Planningcited by 1
  9. 2025Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentationcited by 1
  10. 2021Nested-block self-attention for robust radiotherapy planning segmentationcited by 0
  11. 2021Dual Shape Guided Segmentation Network for Organs-at-Risk in Head and Neck CT Imagescited by 0
  12. 2025Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuningcited by 0