AIMC Topic: Brachytherapy

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Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images.

Medical physics
PURPOSE: Needle-based procedures for diagnosing and treating prostate cancer, such as biopsy and brachytherapy, have incorporated three-dimensional (3D) transrectal ultrasound (TRUS) imaging to improve needle guidance. Using these images effectively ...

Automatic detection of brachytherapy seeds in 3D ultrasound images using a convolutional neural network.

Physics in medicine and biology
A novel approach for automatic localization of brachytherapy seeds in 3D transrectal ultrasound (TRUS) images, using machine learning based algorithm, is presented. 3D radiofrequency ultrasound signals were collected from 13 patients using the linear...

Deep-learning assisted automatic digitization of interstitial needles in 3D CT image based high dose-rate brachytherapy of gynecological cancer.

Physics in medicine and biology
Digitization of interstitial needles is a complicated and tedious process for the treatment planning of 3D CT image based interstitial high dose-rate brachytherapy (HDRBT) of gynecological cancer. We developed a deep-learning assisted auto-digitizati...

Generative adversarial networks (GAN) for compact beam source modelling in Monte Carlo simulations.

Physics in medicine and biology
A method is proposed and evaluated to model large and inconvenient phase space files used in Monte Carlo simulations by a compact generative adversarial network (GAN). The GAN is trained based on a phase space dataset to create a neural network, call...

Fully automatic catheter segmentation in MRI with 3D convolutional neural networks: application to MRI-guided gynecologic brachytherapy.

Physics in medicine and biology
External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-tissue contrast provided by magnetic resonance imaging (MRI) makes it a valuable imaging modality fo...

Deep-learning-assisted automatic digitization of applicators in 3D CT image-based high-dose-rate brachytherapy of gynecological cancer.

Brachytherapy
PURPOSE: Applicator digitization is one of the most critical steps in 3D high-dose-rate brachytherapy (HDRBT) treatment planning. Motivated by recent advances in deep-learning, we propose a deep-learning-assisted applicator digitization method for 3D...

Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images.

Medical image analysis
The goal of this work was to develop a method for accurate and robust automatic segmentation of the prostate clinical target volume in transrectal ultrasound (TRUS) images for brachytherapy. These images can be difficult to segment because of weak or...

Spatio-temporal deep learning models for tip force estimation during needle insertion.

International journal of computer assisted radiology and surgery
PURPOSE: Precise placement of needles is a challenge in a number of clinical applications such as brachytherapy or biopsy. Forces acting at the needle cause tissue deformation and needle deflection which in turn may lead to misplacement or injury. He...

Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer.

Physics in medicine and biology
Inverse treatment planning in radiation therapy is formulated as solving optimization problems. The objective function and constraints consist of multiple terms designed for different clinical and practical considerations. Weighting factors of these ...

A machine-learning-based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies.

Brachytherapy
PURPOSE: External beam radiotherapy combined with interstitial brachytherapy is commonly used to treat patients with bulky, advanced gynecologic cancer. However, the high radiation dose needed to control the tumor may result in fistula development. T...