# The use of deep learning in interventional radiotherapy (brachytherapy): a review with a focus on open source and open data

arXiv: https://arxiv.org/abs/2205.07516 · HTML: https://mlsanity.com/paper/2205.07516/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 176 references that are in the map.
- First posted on arXiv 16 May 2022.

## Builds on (most cited first, 24 of 24)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [Deep Learning in Medical Image Registration: A Survey](https://mlsanity.com/paper/1903.02026/) (2019)
- [Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains](https://mlsanity.com/paper/2007.02035/) (2020)
- [MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data](https://mlsanity.com/paper/2002.03366/) (2020)
- [Deep Learning in Medical Image Registration: A Review](https://mlsanity.com/paper/1912.12318/) (2019)
- [Image biomarker standardisation initiative](https://mlsanity.com/paper/1612.07003/) (2016)
- [Label-driven weakly-supervised learning for multimodal deformable image registration](https://mlsanity.com/paper/1711.01666/) (2017)
- [End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction](https://mlsanity.com/paper/2101.03244/) (2021)
- [Learning Deep Similarity Metric for 3D MR-TRUS Registration](https://mlsanity.com/paper/1806.04548/) (2018)
- [Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound](https://mlsanity.com/paper/1907.01743/) (2019)
- [Cross-modal Attention for MRI and Ultrasound Volume Registration](https://mlsanity.com/paper/2107.04548/) (2021)
- [End-to-end Ultrasound Frame to Volume Registration](https://mlsanity.com/paper/2107.06449/) (2021)
- [Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images](https://mlsanity.com/paper/1612.01655/) (2016)
- [Joint Registration and Segmentation via Multi-Task Learning for Adaptive Radiotherapy of Prostate Cancer](https://mlsanity.com/paper/2105.01844/) (2021)
- [ModelHub.AI: Dissemination Platform for Deep Learning Models](https://mlsanity.com/paper/1911.13218/) (2019)
- [Prostate motion modelling using biomechanically-trained deep neural networks on unstructured nodes](https://mlsanity.com/paper/2007.04972/) (2020)
- [ARPM-net: A novel CNN-based adversarial method with Markov Random Field enhancement for prostate and organs at risk segmentation in pelvic CT images](https://mlsanity.com/paper/2008.04488/) (2020)
- [Deep learning in magnetic resonance prostate segmentation: A review and a new perspective](https://mlsanity.com/paper/2011.07795/) (2020)
- [Learning Multi-Modal Volumetric Prostate Registration with Weak Inter-Subject Spatial Correspondence](https://mlsanity.com/paper/2102.04938/) (2021)
- [A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network](https://mlsanity.com/paper/1903.12331/) (2019)
- [Rapid treatment planning for low-dose-rate prostate brachytherapy with TP-GAN](https://mlsanity.com/paper/2103.09996/) (2021)
- [Automatic Segmentation of the Prostate on 3D Trans-rectal Ultrasound Images using Statistical Shape Models and Convolutional Neural Networks](https://mlsanity.com/paper/2106.09662/) (2021)
- [Automatic 3D Ultrasound Segmentation of Uterus Using Deep Learning](https://mlsanity.com/paper/2109.09283/) (2021)

## Abstract (arXiv)

Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in interventional radiotherapy (brachytherapy) deep learning is still in an early phase. In this review, first, we investigated and scrutinised the role of deep learning in all processes of interventional radiotherapy and directly related fields. Additionally we summarised the most recent developments. To reproduce results of deep learning algorithms both source code and training data must be available. Therefore, a second focus of this work was on the analysis of the availability of open source, open data and open models. In our analysis, we were able to show that deep learning plays already a major role in some areas of interventional radiotherapy, but is still hardly presented in others. Nevertheless, its impact is increasing with the years, partly self-propelled but also influenced by closely related fields. Open source, data and models are growing in number but are still scarce and unevenly distributed among different research groups. The reluctance in publishing code, data and models limits reproducibility and restricts evaluation to mono-institutional datasets. Summarised, deep learning will change positively the workflow of interventional radiotherapy but there is room for improvement when it comes to reproducible results and standardised evaluation methods.