Competing for Pixels: A Self-play Algorithm for Weakly-supervised Semantic Segmentation.

Weakly-supervised semantic segmentation (WSSS) methods, reliant on image-level labels indicating object presence, lack explicit correspondence between labels and regions of interest (ROIs), posing a significant challenge. Despite this, WSSS methods have attracted attention due to their much lower annotation costs compared to fully-supervised segmentation. Leveraging reinforcement learning (RL) self-play, we propose a novel WSSS method that gamifies image segmentation of a ROI.

Time conditioning for arbitrary contrast phase generation in interventional computed tomography.

Minimally invasive ablation techniques for renal cancer are becoming more popular due to their low complication rate and rapid recovery period. Despite excellent visualisation, one drawback of the use of computed tomography (CT) in these procedures is the requirement for iodine-based contrast agents, which are associated with adverse reactions and require a higher x-ray dose. The purpose of this work is to examine the use of time information to generate synthetic contrast enhanced images at arbitrary points after contrast agent injection from non-contrast CT images acquired during renal cryoablation cases.

Active learning using adaptable task-based prioritisation.

Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert annotation, for label-efficient model training. We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning, for multi-class segmentation tasks.

The distinct roles of reinforcement learning between pre-procedure and intra-procedure planning for prostate biopsy.

Purpose: Magnetic resonance (MR) imaging targeted prostate cancer (PCa) biopsy enables precise sampling of MR-detected lesions, establishing its importance in recommended clinical practice. Planning for the ultrasound-guided procedure involves pre-selecting needle sampling positions. However, performing this procedure is subject to a number of factors, including MR-to-ultrasound registration, intra-procedure patient movement and soft tissue motions.

Combiner and HyperCombiner networks: Rules to combine multimodality MR images for prostate cancer localisation.

One of the distinct characteristics of radiologists reading multiparametric prostate MR scans, using reporting systems like PI-RADS v2.1, is to score individual types of MR modalities, including 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. This work aims to demonstrate that it is feasible for low-dimensional parametric models to model such decision rules in the proposed Combiner networks, without compromising the accuracy of predicting radiologic labels.

Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker.

Objective: Reconstructing freehand ultrasound in 3D without any external tracker has been a long-standing challenge in ultrasound-assisted procedures. We aim to define new ways of parameterising long-term dependencies, and evaluate the performance.

Methods: First, long-term dependency is encoded by transformation positions within a frame sequence.

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. This work describes a fully 3D prototypical few-shot segmentation algorithm, such that the trained networks can be effectively adapted to clinically interesting structures that are absent in training, using only a few labelled images from a different institute. First, to compensate for the widely recognised spatial variability between institutions in episodic adaptation of novel classes, a novel spatial registration mechanism is integrated into prototypical learning, consisting of a segmentation head and an spatial alignment module.

Prostate cancer segmentation from MRI by a multistream fusion encoder.

Background: Targeted prostate biopsy guided by multiparametric magnetic resonance imaging (mpMRI) detects more clinically significant lesions than conventional systemic biopsy. Lesion segmentation is required for planning MRI-targeted biopsies. The requirement for integrating image features available in T2-weighted and diffusion-weighted images poses a challenge in prostate lesion segmentation from mpMRI.

Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy.

Purpose: Minimally invasive treatments for renal carcinoma offer a low rate of complications and quick recovery. One drawback of the use of computed tomography (CT) for needle guidance is the use of iodinated contrast agents, which require an increased X-ray dose and can potentially cause adverse reactions. The purpose of this work is to generalise the problem of synthetic contrast enhancement to allow the generation of multiple phases on non-contrast CT data from a real-world, clinical dataset without training multiple convolutional neural networks.

Meta-Learning Initializations for Interactive Medical Image Registration.

We present a meta-learning framework for interactive medical image registration. Our proposed framework comprises three components: a learning-based medical image registration algorithm, a form of user interaction that refines registration at inference, and a meta-learning protocol that learns a rapidly adaptable network initialization. This paper describes a specific algorithm that implements the registration, interaction and meta-learning protocol for our exemplar clinical application: registration of magnetic resonance (MR) imaging to interactively acquired, sparsely-sampled transrectal ultrasound (TRUS) images.

Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study.

Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.

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. As an example, we focus on aligning intra-subject multiparametric Magnetic Resonance (mpMR) images, between T2-weighted (T2w) scans and diffusion-weighted scans with high b-value (DWI [Formula: see text]). For the application of localising tumours in mpMR images, diffusion scans with zero b-value (DWI [Formula: see text]) are considered easier to register to T2w due to the availability of corresponding features.

Imaging features for the prediction of clinical endpoints in chronic liver disease: a scoping review protocol.

Introduction: Chronic liver disease is a growing cause of morbidity and mortality in the UK. Acute presentation with advanced disease is common and prioritisation of resources to those at highest risk at earlier disease stages is essential to improving patient outcomes. Existing prognostic tools are of limited accuracy and to date no imaging-based tools are used in clinical practice, despite multiple anatomical imaging features that worsen with disease severity.

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. When the task is performed using machine learning algorithms, such as a neural-network-based task predictor for image classification or segmentation, the performance of the task predictor provides an objective estimate of task amenability. In this work, we use an IQA controller to predict the task amenability which, itself being parameterised by neural networks, can be trained simultaneously with the task predictor.

Voice-Assisted Image Labeling for Endoscopic Ultrasound Classification Using Neural Networks.

Ultrasound imaging is a commonly used technology for visualising patient anatomy in real-time during diagnostic and therapeutic procedures. High operator dependency and low reproducibility make ultrasound imaging and interpretation challenging with a steep learning curve. Automatic image classification using deep learning has the potential to overcome some of these challenges by supporting ultrasound training in novices, as well as aiding ultrasound image interpretation in patient with complex pathology for more experienced practitioners.

Real-time multimodal image registration with partial intraoperative point-set data.

We present Free Point Transformer (FPT) - a deep neural network architecture for non-rigid point-set registration. Consisting of two modules, a global feature extraction module and a point transformation module, FPT does not assume explicit constraints based on point vicinity, thereby overcoming a common requirement of previous learning-based point-set registration methods. FPT is designed to accept unordered and unstructured point-sets with a variable number of points and uses a "model-free" approach without heuristic constraints.

Prostate Radiofrequency Focal Ablation (ProRAFT) Trial: A Prospective Development Study Evaluating a Bipolar Radiofrequency Device to Treat Prostate Cancer.

Purpose: We determined the early efficacy of bipolar radiofrequency ablation with a coil design for focal ablation of clinically significant localized prostate cancer visible at multiparametric magnetic resonance imaging.

Materials And Methods: A prospective IDEAL phase 2 development study (Focal Prostate Radiofrequency Ablation, NCT02294903) recruited treatment-naïve patients with a single focus of significant localized prostate cancer (Gleason 7 or 4 mm or more of Gleason 6) concordant with a lesion visible on multiparametric magnetic resonance imaging. Intervention was a focal ablation with a bipolar radiofrequency system (Encage™) encompassing the lesion and a predefined margin using nonrigid magnetic resonance imaging-ultrasound fusion.

False Positive Multiparametric Magnetic Resonance Imaging Phenotypes in the Biopsy-naïve Prostate: Are They Distinct from Significant Cancer-associated Lesions? Lessons from PROMIS.

Background: False positive multiparametric magnetic resonance imaging (mpMRI) phenotypes prompt unnecessary biopsies. The Prostate MRI Imaging Study (PROMIS) provides a unique opportunity to explore such phenotypes in biopsy-naïve men with raised prostate-specific antigen (PSA) and suspected cancer.

Objective: To compare mpMRI lesions in men with/without significant cancer on transperineal mapping biopsy (TPM).

Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration.

Convolutional neural networks (CNNs) have recently led to significant advances in automatic segmentations of anatomical structures in medical images, and a wide variety of network architectures are now available to the research community. For applications such as segmentation of the prostate in magnetic resonance images (MRI), the results of the PROMISE12 online algorithm evaluation platform have demonstrated differences between the best-performing segmentation algorithms in terms of numerical accuracy using standard metrics such as the Dice score and boundary distance. These small differences in the segmented regions/boundaries outputted by different algorithms may potentially have an unsubstantial impact on the results of downstream image analysis tasks, such as estimating organ volume and multimodal image registration, which inform clinical decisions.

Integration of spatial information in convolutional neural networks for automatic segmentation of intraoperative transrectal ultrasound images.

Image guidance systems that register scans of the prostate obtained using transrectal ultrasound (TRUS) and magnetic resonance imaging are becoming increasingly popular as a means of enabling tumor-targeted prostate cancer biopsy and treatment. However, intraoperative segmentation of TRUS images to define the three-dimensional (3-D) geometry of the prostate remains a necessary task in existing guidance systems, which often require significant manual interaction and are subject to interoperator variability. Therefore, automating this step would lead to more acceptable clinical workflows and greater standardization between different operators and hospitals.

The SmartTarget Biopsy Trial: A Prospective, Within-person Randomised, Blinded Trial Comparing the Accuracy of Visual-registration and Magnetic Resonance Imaging/Ultrasound Image-fusion Targeted Biopsies for Prostate Cancer Risk Stratification.

Background: Multiparametric magnetic resonance imaging (mpMRI)-targeted prostate biopsies can improve detection of clinically significant prostate cancer and decrease the overdetection of insignificant cancers. It is unknown whether visual-registration targeting is sufficient or augmentation with image-fusion software is needed.

Objective: To assess concordance between the two methods.

Immunohistochemical biomarker validation in highly selective needle biopsy microarrays derived from mpMRI-characterized prostates.

Introduction: Diagnosing prostate cancer routinely involves tissue biopsy and increasingly image guided biopsy using multiparametric MRI (mpMRI). Excess tissue after diagnosis can be used for research to improve the diagnostic pathway and the vertical assembly of prostate needle biopsy cores into tissue microarrays (TMAs) allows the parallel immunohistochemical (IHC) validation of cancer biomarkers in routine diagnostic specimens. However, tissue within a biopsy core is often heterogeneous and cancer is not uniformly present, resulting in needle biopsy TMAs that suffer from highly variable cancer detection rates that complicate parallel biomarker validation.

Accuracy of Transperineal Targeted Prostate Biopsies, Visual Estimation and Image Fusion in Men Needing Repeat Biopsy in the PICTURE Trial.

Purpose: We evaluated the detection of clinically significant prostate cancer using magnetic resonance imaging targeted biopsies and compared visual estimation to image fusion targeting in patients requiring repeat prostate biopsies.

Materials And Methods: The prospective, ethics committee approved PICTURE trial (ClinicalTrials.gov NCT01492270) enrolled 249 consecutive patients from January 11, 2012 to January 29, 2014.

Weakly-supervised convolutional neural networks for multimodal image registration.

One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence.