# RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy

arXiv: https://arxiv.org/abs/2106.11942 · HTML: https://mlsanity.com/paper/2106.11942/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 76 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Deep Learning is Robust to Massive Label Noise](https://mlsanity.com/paper/1705.10694/) (2017)
- [Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation](https://mlsanity.com/paper/1702.07841/) (2017)
- [Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections](https://mlsanity.com/paper/1911.12709/) (2019)
- [Automated Design of Deep Learning Methods for Biomedical Image Segmentation](https://mlsanity.com/paper/1904.08128/) (2019)
- [Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks](https://mlsanity.com/paper/1805.06349/) (2018)
- [Imperfect Segmentation Labels: How Much Do They Matter?](https://mlsanity.com/paper/1806.04618/) (2018)
- [Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment](https://mlsanity.com/paper/2007.01383/) (2020)
- [Segmentation of Roots in Soil with U-Net](https://mlsanity.com/paper/1902.11050/) (2019)
- [On Interactive Machine Learning and the Potential of Cognitive Feedback](https://mlsanity.com/paper/2003.10365/) (2020)
- [Multiple resolution residual network for automatic thoracic organs-at-risk segmentation from CT](https://mlsanity.com/paper/2005.13690/) (2020)
- [Icon: An Interactive Approach to Train Deep Neural Networks for Segmentation of Neuronal Structures](https://mlsanity.com/paper/1610.09032/) (2016)

## Built on by (most cited first, 1 of 1)

- [Localise to segment: crop to improve organ at risk segmentation accuracy](https://mlsanity.com/paper/2304.04606/) (2023)

## Abstract (arXiv)

Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate the accuracy and time-savings resulting from the use of an interactive-machine-learning method for an organ-at-risk contouring task. We compare the method to the Eclipse contouring software and find strong agreement with manual delineations, with a dice score of 0.95. The annotations created using corrective-annotation also take less time to create as more images are annotated, resulting in substantial time savings compared to manual methods, with hearts that take 2 minutes and 2 seconds to delineate on average, after 923 images have been delineated, compared to 7 minutes and 1 seconds when delineating manually. Our experiment demonstrates that interactive-machine-learning with corrective-annotation provides a fast and accessible way for non computer-scientists to train deep-learning models to segment their own structures of interest as part of routine clinical workflows. Source code is available at hrefhttps://github.com/Abe404/RootPainter3Dthis HTTPS URL.