# Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

arXiv: https://arxiv.org/abs/1809.04430 · HTML: https://mlsanity.com/paper/1809.04430/

## Key facts

- Cited by 77 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 30 of 77)

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- [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)
- [Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation](https://mlsanity.com/paper/2107.04914/) (2021)
- [Assessing YOLACT++ for real time and robust instance segmentation of medical instruments in endoscopic procedures](https://mlsanity.com/paper/2103.15997/) (2021)
- [Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentation](https://mlsanity.com/paper/2201.01636/) (2022)
- [Primitive Geometry Segment Pre-training for 3D Medical Image Segmentation](https://mlsanity.com/paper/2401.03665/) (2024)
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- [Feather-Light Fourier Domain Adaptation in Magnetic Resonance Imaging](https://mlsanity.com/paper/2208.00474/) (2022)
- [Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers](https://mlsanity.com/paper/2209.11268/) (2022)
- [Anatomical feature-prioritized loss for enhanced MR to CT translation](https://mlsanity.com/paper/2410.10328/) (2024)
- [Learning Fuzzy Clustering for SPECT/CT Segmentation via Convolutional Neural Networks](https://mlsanity.com/paper/2104.08623/) (2021)
- [Automatic identification of segmentation errors for radiotherapy using geometric learning](https://mlsanity.com/paper/2206.13317/) (2022)
- [USE-Evaluator: Performance Metrics for Medical Image Segmentation Models with Uncertain, Small or Empty Reference Annotations](https://mlsanity.com/paper/2209.13008/) (2022)
- [Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation](https://mlsanity.com/paper/2307.15645/) (2023)

## Abstract (arXiv)

Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manual time consuming delineation of radio-sensitive organs at risk (OARs). This planning process can delay treatment, while also introducing inter-operator variability with resulting downstream radiation dose differences. While auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying and achieving expert performance remain. Adopting a deep learning approach, we demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck OARs commonly segmented in clinical practice. The model was trained on a dataset of 663 deidentified computed tomography (CT) scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus OAR definitions. We demonstrate the model's clinical applicability by assessing its performance on a test set of 21 CT scans from clinical practice, each with the 21 OARs segmented by two independent experts. We also introduce surface Dice similarity coefficient (surface DSC), a new metric for the comparison of organ delineation, to quantify deviation between OAR surface contours rather than volumes, better reflecting the clinical task of correcting errors in the automated organ segmentations. The model's generalisability is then demonstrated on two distinct open source datasets, reflecting different centres and countries to model training. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.