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A list of machine and deep learning publications in interventional radiotherapy and related fields

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# BrachyAndAI

## Update
A more recent and comprehensive summary of deep learning in interventional radiotherapy can be found in our article [The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data](https://www.sciencedirect.com/science/article/pii/S093938892200099X) or in the preprint version on Arxiv [https://arxiv.org/abs/2205.07516]. The article includes all the articles listed below, as well as a number of recent articles.

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The following listing covers all the articles I mentioned in the talk "Maschinelles Lernen in der interventionellen Radioonkologie" at the [DEGRO 2021](https://degro-jahrestagung.meta-dcr.com/degro2021/crs/maschinelles-lernen-in-der-interventionellen-radioonkologie).

## General
- Artificial intelligence (AI) and interventional radiotherapy (brachytherapy): state of art and future perspectives [<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7701925/>]
- A Review of the Application of Deep Learning in Brachytherapy [<https://www.scirp.org/journal/paperinformation.aspx?paperid=101800>]

## Segmentation
### MRI
- Graph-convolutional-network-based interactive prostate segmentation in MR images [<https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.14327?campaign=wolacceptedarticle>]
- Deep learning in magnetic resonance prostate segmentation: A review and a new perspective [<https://arxiv.org/pdf/2011.07795.pdf>]
- Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods [<https://www.sciencedirect.com/science/article/abs/pii/S0169260719310442?via%3Dihub>]
- Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net [<https://www.nature.com/articles/s41598-020-71080-0>]
- Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment [<https://pubs.rsna.org/doi/10.1148/radiol.2019190938>]
- MRI and CT bladder segmentation from classical to deep learning based approaches: Current limitations and lessons [<https://www.sciencedirect.com/science/article/abs/pii/S0010482521002663>]; [<https://arxiv.org/pdf/2101.06498.pdf>]
- Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy [<https://ro-journal.biomedcentral.com/articles/10.1186/s13014-020-01528-0>]
- Bladder segmentation based on deep learning approaches:current limitations and lessons: [<https://arxiv.org/pdf/2101.06498.pdf>]
- A Deep Learning-Based Approach for Accurate Segmentation of Bladder Wall using MR Images [<https://ieeexplore.ieee.org/document/9010233>]
- Machine Segmentation of Pelvic Anatomy in MRI-Assisted Radiosurgery (MARS) for Prostate Cancer Brachytherapy[<https://www.sciencedirect.com/science/article/abs/pii/S0360301620313961?via%3Dihub>]
### CT
- Bladder, Rectum, Samenblase, Prostata: Joint Registration and Segmentation via Multi-Task Learning for Adaptive Radiotherapy of Prostate Cancer [<https://arxiv.org/pdf/2105.01844.pdf>]
- Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer [<https://www.sciencedirect.com/science/article/abs/pii/S0167814021061703>]
- Pelvic multi-organ segmentation on cone-beam CT for prostate adaptive radiotherapy [<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429321/>]
- MetricUNet: Synergistic image- and voxel-level learning for precise prostate segmentation via online sampling [<https://www.sciencedirect.com/science/article/abs/pii/S1361841521000852?dgcid=raven_sd_aip_email>]; [<https://arxiv.org/abs/2005.07462>]
- 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://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.14580>]; [<https://arxiv.org/abs/2008.04488>]
- Automatic Segmentation of the Prostate on CT Images Using Deep Neural Networks (DNN) [<https://www.sciencedirect.com/science/article/pii/S0360301619303761>]
- CT prostate segmentation based on synthetic MRI-aided deep attention fully convolution network [<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764436/>]
- Automatic segmentation and applicator reconstruction forCT-based brachytherapy of cervical cancer using 3Dconvolutional neural networks [<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592978/pdf/ACM2-21-158.pdf>]
### TRUS
- Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound [<https://ieeexplore.ieee.org/document/8698868>]; [<https://github.com/wulalago/DAF3D>]
- A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy [<https://link.springer.com/article/10.1007/s11548-020-02231-x?utm_source=toc&utm_medium=email&utm_campaign=toc_11548_15_9&utm_content=etoc_springer_20200812>]
- Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images [<https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.14134>]
- Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images [<https://www.sciencedirect.com/science/article/abs/pii/S1361841519300623>]
- Deep Learning for Real-time, Automatic, and Scanner-adapted Prostate (Zone) Segmentation of Transrectal Ultrasound, for Example, Magnetic Resonance Imaging–transrectal Ultrasound Fusion Prostate Biopsy [<https://www.sciencedirect.com/science/article/pii/S2405456919301257?via%3Dihub>]
- Study on automatic detection and classification of breast nodule using deep convolutional neural network system [<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7578508/>]
- Methods for the segmentation and classification of breast ultrasound images: a review [<https://link.springer.com/content/pdf/10.1007/s40477-020-00557-5.pdf>]
- An RDAU-NET model for lesion segmentation in breast ultrasound images [<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6707567/pdf/pone.0221535.pdf>]
- Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images [<https://www.sciencedirect.com/science/article/abs/pii/S1361841519300623>]
### PET
- Intraprostatic Tumour Segmentation on PSMA-PET Images in Patients with Primary Prostate Cancer with a Convolutional Neural Network [<https://pubmed.ncbi.nlm.nih.gov/33127624/>]
### Needles
- Deep learning applications in automatic needle segmentation in ultrasound-guided prostate brachytherapy [<https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.14328>]
- Deep learning‐based digitization of prostate brachytherapy needles in ultrasound images [<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7821271/>]
- Deep-learning–assisted automatic digitization of applicators in 3D CT image-based high-dose-rate brachytherapy of gynecological cancer [<https://www.brachyjournal.com/article/S1538-4721(19)30098-4/abstract>]
- Automated Needle Digitization in Ultrasound-based Prostate High Dose-rate Brachytherapy Using a Deep Learning Algorithm [<https://www.redjournal.org/article/S0360-3016(20)32276-8/fulltext>]

## Registration
- A Cross-Stitch Architecture for Joint Registration and Segmentation in Adaptive Radiotherapy [<https://2020.midl.io/papers/beljaards20.html>]
- LABEL-DRIVEN WEAKLY-SUPERVISED LEARNING FOR MULTIMODAL DEFORMABLE IMAGE REGISTRATION [<https://arxiv.org/ftp/arxiv/papers/1711/1711.01666.pdf>] [<https://ieeexplore.ieee.org/document/8363756>]
- Learning deep similarity metric for 3D MR–TRUS image registration [<https://link.springer.com/article/10.1007/s11548-018-1875-7>]
- Label-driven magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) registration using weakly supervised learning for MRI-guided prostate radiotherapy [<https://iopscience.iop.org/article/10.1088/1361-6560/ab8cd6>]
- Biomechanically Constrained Non-rigid MR-TRUS Prostate Registration using Deep Learning based 3D Point Cloud Matching [<https://www.sciencedirect.com/science/article/abs/pii/S1361841520302097?dgcid=raven_sd_aip_email>]
- MR to ultrasound image registration with segmentation-based learning for HDR prostate brachytherapy [<https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.14901>]
- Prostate motion modelling using biomechanically-trained deep neural networks on unstructured nodes [<https://arxiv.org/pdf/2007.04972.pdf>]
- Deformable MR-CBCT prostate registration using biomechanically constrained deep learning networks [<https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.14584>]
- Deformable registration of PET/CT and ultrasound for disease-targeted focal prostate brachytherapy [<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6739636/>]

## Treatment Planning
- Knowledge-Based Three-Dimensional Dose Prediction for Tandem-And-Ovoid Brachytherapy [<https://arxiv.org/pdf/2102.11384.pdf>]
- A knowledge-based organ dose prediction tool for brachytherapy treatment planning of patients with cervical cancer [<https://pubmed.ncbi.nlm.nih.gov/32513446/>]
- Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer [<https://pubmed.ncbi.nlm.nih.gov/30978709/>]
- Conventional vs machine learning–based treatment planning in prostate brachytherapy: Results of a Phase I randomized controlled trial [<https://www.sciencedirect.com/science/article/pii/S1538472120300490>] [<https://www.brachyjournal.com/article/S1538-4721(20)30049-0/pdf>]
- Evaluation of a Machine-Learning Algorithm for Treatment Planning in Prostate Low-Dose-Rate Brachytherapy [<https://pubmed.ncbi.nlm.nih.gov/28244419/>]
- Development of a Machine Learning Model for Optimal Applicator Selection in High-Dose-Rate Cervical Brachytherapy [<https://www.frontiersin.org/articles/10.3389/fonc.2021.611437/full>]
- Prostate Dose Prediction in HDR Brachytherapy using Unsupervised Multi-Atlas Fusion [<https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11596/115962C/Prostate-dose-prediction-in-HDR-Brachytherapy-using-unsupervised-multi-atlas/10.1117/12.2580979.full?SSO=1>]
- RapidBrachyDL: Rapid Radiation Dose Calculations in Brachytherapy Via Deep Learning [<https://www.sciencedirect.com/science/article/abs/pii/S0360301620311226>]
- Intraoperative optimization of seed implantation plan in breast brachytherapy [https://link.springer.com/article/10.1007/s11548-021-02350-z?utm_source=toc&utm_medium=email&utm_campaign=toc_11548_16_6&utm_content=etoc_springer_20210601]

## Outcome Prediction
- Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment [<https://pubs.rsna.org/doi/10.1148/radiol.2019190938?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub++0pubmed&>] [<https://github.com/MIC-DKFZ/PROUNET>]
- Deep Radiomic Analysis to Predict Gleason Score in Prostate Cancer [<https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9195417>]
- Salvage HDR Brachytherapy: Multiple Hypothesis Testing Versus Machine Learning Analysis [<https://pubmed.ncbi.nlm.nih.gov/29709315/>]
- A deep dive into understanding tumor foci classification using multiparametric MRI based on convolutional neural network [<https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.14255?campaign=wolearlyview>]
- Radiomic features from PSMA PET for non-invasive intraprostatic tumor discrimination and characterization in patients with intermediate- and high-risk prostate cancer - a comparison study with histology reference [<https://pubmed.ncbi.nlm.nih.gov/31131055/>]
- Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer [<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5963260/>]
- Texture analysis of pretreatment [18F]FDG PET/CT for the prognostic prediction of locally advanced salivary gland carcinoma treated with interstitial brachytherapy [<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6738371/>]
- Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes [<https://www.sciencedirect.com/science/article/abs/pii/S1120179717304787?via%3Dihub>]
- Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [18F]-fluorodeoxyglucose positron emission tomography/computed tomography [<https://link.springer.com/article/10.1007%2Fs00330-019-06265-x>]
- [18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation [<https://link.springer.com/article/10.1007/s00259-021-05303-5>]
- Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study [<https://pubmed.ncbi.nlm.nih.gov/28914611/>]

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