# Assisted Probe Positioning for Ultrasound Guided Radiotherapy Using Image Sequence Classification

arXiv: https://arxiv.org/abs/2010.02732 · HTML: https://mlsanity.com/paper/2010.02732/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory](https://mlsanity.com/paper/2209.06952/) (2022)
- [EndoARSS: Adapting Spatially-Aware Foundation Model for Efficient Activity Recognition and Semantic Segmentation in Endoscopic Surgery](https://mlsanity.com/paper/2506.06830/) (2025)

## Abstract (arXiv)

Effective transperineal ultrasound image guidance in prostate external beam radiotherapy requires consistent alignment between probe and prostate at each session during patient set-up. Probe placement and ultrasound image inter-pretation are manual tasks contingent upon operator skill, leading to interoperator uncertainties that degrade radiotherapy precision. We demonstrate a method for ensuring accurate probe placement through joint classification of images and probe position data. Using a multi-input multi-task algorithm, spatial coordinate data from an optically tracked ultrasound probe is combined with an image clas-sifier using a recurrent neural network to generate two sets of predictions in real-time. The first set identifies relevant prostate anatomy visible in the field of view using the classes: outside prostate, prostate periphery, prostate centre. The second set recommends a probe angular adjustment to achieve alignment between the probe and prostate centre with the classes: move left, move right, stop. The algo-rithm was trained and tested on 9,743 clinical images from 61 treatment sessions across 32 patients. We evaluated classification accuracy against class labels de-rived from three experienced observers at 2/3 and 3/3 agreement thresholds. For images with unanimous consensus between observers, anatomical classification accuracy was 97.2% and probe adjustment accuracy was 94.9%. The algorithm identified optimal probe alignment within a mean (standard deviation) range of 3.7circ (1.2circ) from angle labels with full observer consensus, comparable to the 2.8circ (2.6circ) mean interobserver range. We propose such an algorithm could assist ra-diotherapy practitioners with limited experience of ultrasound image interpreta-tion by providing effective real-time feedback during patient set-up.