mlsanityData to 7 Oct 2026

FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images

arXiv 2104.01771 ↗First posted 5 Apr 2021eess.IV cs.CVcited by 17

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Abstract (arXiv)

Radiotherapy is a treatment where radiation is used to eliminate cancer cells. The delineation of organs-at-risk (OARs) is a vital step in radiotherapy treatment planning to avoid damage to healthy organs. For nasopharyngeal cancer, more than 20 OARs are needed to be precisely segmented in advance. The challenge of this task lies in complex anatomical structure, low-contrast organ contours, and the extremely imbalanced size between large and small organs. Common segmentation methods that treat them equally would generally lead to inaccurate small-organ labeling. We propose a novel two-stage deep neural network, FocusNetv2, to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs with specifically designed small-organ localization and segmentation sub-networks while maintaining the accuracy of large organ segmentation. In addition to our original FocusNet, we employ a novel adversarial shape constraint on small organs to ensure the consistency between estimated small-organ shapes and organ shape prior knowledge. Our proposed framework is extensively tested on both self-collected dataset of 1,164 CT scans and the MICCAI Head and Neck Auto Segmentation Challenge 2015 dataset, which shows superior performance compared with state-of-the-art head and neck OAR segmentation methods.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 26 of its 56 references that are in the map. First posted on arXiv 5 Apr 2021.

Builds on

26 of 56 references.

References · 26

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14 more
  1. 2016Generative Visual Manipulation on the Natural Image Manifoldcited by 348
  2. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
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  8. 20163D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumescited by 43
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  11. 2019FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Imagescited by 24
  12. 2017Automatic Liver Segmentation Using an Adversarial Image-to-Image Networkcited by 17
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 5; 2024: 2; 2025: 3; 2026: 1.

17 papers.

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  1. 2021UTNet: A Hybrid Transformer Architecture for Medical Image Segmentationcited by 116
  2. 2022A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmarkcited by 45
  3. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  4. 2023Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentationcited by 32
  5. 2024Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomographycited by 31
  6. 2023SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinomacited by 28
  7. 2024Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classificationcited by 17
  8. 2022Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentationcited by 6
  9. 2025Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updatescited by 5
  10. 2023Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentationcited by 3
  11. 2023Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotationcited by 2
  12. 2023Fully Automatic Segmentation of Gross Target Volume and Organs-at-Risk for Radiotherapy Planning of Nasopharyngeal Carcinomacited by 2
5 more
  1. 2021Comparing Bayesian Models for Organ Contouring in Head and Neck Radiotherapycited by 1
  2. 2022HMRNet: High and Multi-Resolution Network with Bidirectional Feature Calibration for Brain Structure Segmentation in Radiotherapycited by 1
  3. 2025Improvise, Adapt, Overcome -- Telescopic Adapters for Efficient Fine-tuning of Vision Language Models in Medical Imagingcited by 1
  4. 2025SAS: Segment Anything Small for Ultrasound -- A Non-Generative Data Augmentation Technique for Robust Deep Learning in Ultrasound Imagingcited by 0
  5. 2026HUR-MACL: High-Uncertainty Region-Guided Multi-Architecture Collaborative Learning for Head and Neck Multi-Organ Segmentationcited by 0