# Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search

arXiv: https://arxiv.org/abs/2004.08426 · HTML: https://mlsanity.com/paper/2004.08426/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

## Built on by (most cited first, 23 of 23)

- [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)
- [Deep Implicit Statistical Shape Models for 3D Medical Image Delineation](https://mlsanity.com/paper/2104.02847/) (2021)
- [Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans](https://mlsanity.com/paper/2302.00162/) (2023)
- [SegReg: Segmenting OARs by Registering MR Images and CT Annotations](https://mlsanity.com/paper/2311.06956/) (2023)
- [Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scans](https://mlsanity.com/paper/2403.15063/) (2024)
- [DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search](https://mlsanity.com/paper/2109.09271/) (2021)
- [Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions](https://mlsanity.com/paper/2203.06810/) (2022)
- [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)
- [Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search](https://mlsanity.com/paper/2304.05405/) (2023)
- [Weaving Attention U-net: A Novel Hybrid CNN and Attention-based Method for Organs-at-risk Segmentation in Head and Neck CT Images](https://mlsanity.com/paper/2107.04847/) (2021)
- [A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation](https://mlsanity.com/paper/2309.03906/) (2023)
- [MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising](https://mlsanity.com/paper/2103.12995/) (2021)
- [Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation](https://mlsanity.com/paper/2303.00232/) (2023)
- [Multi-institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume using planning-CT and FDG-PETCT](https://mlsanity.com/paper/2110.05280/) (2021)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification](https://mlsanity.com/paper/2305.02491/) (2023)
- [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)
- [Auto Machine Learning for Medical Image Analysis by Unifying the Search on Data Augmentation and Neural Architecture](https://mlsanity.com/paper/2207.10351/) (2022)
- [Low-Rank Continual Pyramid Vision Transformer: Incrementally Segment Whole-Body Organs in CT with Light-Weighted Adaptation](https://mlsanity.com/paper/2410.04689/) (2024)
- [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)
- [Nested-block self-attention for robust radiotherapy planning segmentation](https://mlsanity.com/paper/2102.13541/) (2021)

## Abstract (arXiv)

OAR segmentation is a critical step in radiotherapy of head and neck (H&N) cancer, where inconsistencies across radiation oncologists and prohibitive labor costs motivate automated approaches. However, leading methods using standard fully convolutional network workflows that are challenged when the number of OARs becomes large, e.g. > 40. For such scenarios, insights can be gained from the stratification approaches seen in manual clinical OAR delineation. This is the goal of our work, where we introduce stratified organ at risk segmentation (SOARS), an approach that stratifies OARs into anchor, mid-level, and small & hard (S&H) categories. SOARS stratifies across two dimensions. The first dimension is that distinct processing pipelines are used for each OAR category. In particular, inspired by clinical practices, anchor OARs are used to guide the mid-level and S&H categories. The second dimension is that distinct network architectures are used to manage the significant contrast, size, and anatomy variations between different OARs. We use differentiable neural architecture search (NAS), allowing the network to choose among 2D, 3D or Pseudo-3D convolutions. Extensive 4-fold cross-validation on 142 H&N cancer patients with 42 manually labeled OARs, the most comprehensive OAR dataset to date, demonstrates that both pipeline- and NAS-stratification significantly improves quantitative performance over the state-of-the-art (from 69.52% to 73.68% in absolute Dice scores). Thus, SOARS provides a powerful and principled means to manage the highly complex segmentation space of OARs.