Search Results for author:
Found 22 papers, 8 papers with code
AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives
Based on the presented experiments from two clinical data sets, consisting of histopathology-labelled MR images from more than 800 and 500 patients in the respective UCLA and UCL PROMIS studies, we show that the proposed strategy can improve the diagnostic accuracy, by augmenting the radiologist reading of the MR imaging.
Nonrigid Reconstruction of Freehand Ultrasound without a Tracker
Motivated by a) the observed nonrigid deformation due to soft tissue motion during scanning, and b) the highly sensitive prediction of rigid transformation, this study investigates the methods and their benefits in predicting nonrigid transformations for reconstructing 3D US.
Semi-weakly-supervised neural network training for medical image registration
For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods, and (b) being used independently in registration tasks in which unsupervised losses are unavailable or ineffective.
Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images
We propose Boundary-RL, a novel weakly supervised segmentation method that utilises only patch-level labels for training.
Combiner and HyperCombiner Networks: Rules to Combine Multimodality MR Images for Prostate Cancer Localisation
One of the distinct characteristics in radiologists' reading of multiparametric prostate MR scans, using reporting systems such as PI-RADS v2. 1, is to score individual types of MR modalities, T2-weighted, diffusion-weighted, and dynamic contrast-enhanced, and then combine these image-modality-specific scores using standardised decision rules to predict the likelihood of clinically significant cancer.
Spatial Correspondence between Graph Neural Network-Segmented Images
Graph neural networks (GNNs) have been proposed for medical image segmentation, by predicting anatomical structures represented by graphs of vertices and edges.
Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion
For the first time, we evaluate the realism of the generated pathology by blind expert identification of the presence of suspected lesions, where we find that the clinician performs similarly for both real and synthesised images, with a 2. 9 percentage point difference in lesion identification accuracy between real and synthesised images, demonstrating the potentials in radiological training purposes.
Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration
The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and expert annotations.
Cross-Modality Image Registration using a Training-Time Privileged Third Modality
In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered.
Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration
Image registration is useful for quantifying morphological changes in longitudinal MR images from prostate cancer patients.
The impact of using voxel-level segmentation metrics on evaluating multifocal prostate cancer localisation
However, the differences in false-positives and false-negatives, between the actual errors and the perceived counterparts if DSC is used, can be as high as 152 and 154, respectively, out of the 357 test set lesions.
Image quality assessment for machine learning tasks using meta-reinforcement learning
In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability.
Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation
This allows for the trained IQA controller to measure the impact an image has on the target task performance, when this task is performed using the predictor, e. g. segmentation and classification neural networks in modern clinical applications.
Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning
The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after.
Adaptable image quality assessment using meta-reinforcement learning of task amenability
Using 6644 clinical ultrasound images from 249 prostate cancer patients, our results for image classification and segmentation tasks show that the proposed IQA method can be adapted using data with as few as respective 19. 7% and 29. 6% expert-reviewed consensus labels and still achieve comparable IQA and task performance, which would otherwise require a training dataset with 100% expert labels.
Development and evaluation of intraoperative ultrasound segmentation with negative image frames and multiple observer labels
When developing deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames that do not contain regions of interest and the high variance in ground-truth labels.
Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images
Prostate cancer (PCa) is one of the leading causes of death for men worldwide.
Learning image quality assessment by reinforcing task amenable data selection
In this paper, we consider a type of image quality assessment as a task-specific measurement, which can be used to select images that are more amenable to a given target task, such as image classification or segmentation.
Morphological Change Forecasting for Prostate Glands using Feature-based Registration and Kernel Density Extrapolation
Organ morphology is a key indicator for prostate disease diagnosis and prognosis.
DeepReg: a deep learning toolkit for medical image registration
DeepReg (https://github. com/DeepRegNet/DeepReg) is a community-supported open-source toolkit for research and education in medical image registration using deep learning.
Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination
Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance program.
MRI Cross-Modality NeuroImage-to-NeuroImage Translation
Based on our proposed framework, we first propose a method for cross-modality registration by fusing the deformation fields to adopt the cross-modality information from translated modalities.
