Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: The relevance of fractionation.

OBJECTIVES: Post-therapy pneumonitis (PTP) is a relevant side effect of thoracic radiotherapy and immunotherapy with checkpoint inhibitors (ICI). The influence of the combination of both, including dose fractionation schemes on PTP development is still unclear. This study aims to improve the PTP risk estimation after radio(chemo)therapy (R(C)T) for lung cancer with and without ICI by investigation of the impact of dose fractionation on machine learning (ML)-based prediction. MATERIALS AND METHODS: Data from 100 patients who received fractionated R(C)T were collected. 39 patients received additional ICI therapy. Computed Tomography (CT), RT segmentation and dose data were extracted and physical doses were converted to 2-Gy equivalent doses (EQD2) to account for different fractionation schemes. Features were reduced using Pearson intercorrelation and the Boruta algorithm within 1000-fold bootstrapping. Six single (clinics, Dose Volume Histogram (DVH), ICI, chemotherapy, radiomics, dosiomics) and four combined models (radiomics + dosiomics, radiomics + DVH + Clinics, dosiomics + DVH + Clinics, radiomics + dosiomics + DVH + Clinics) were trained to predict PTP. Dose-based models were tested using physical dose and EQD2. Four ML-algorithms (random forest (rf), logistic elastic net regression, support vector machine, logitBoost) were trained and tested using 5-fold nested cross validation and Synthetic Minority Oversampling Technique (SMOTE) for resampling in R. Prediction was evaluated using the area under the receiver operating characteristic curve (AUC) on the test sets of the outer folds. RESULTS: The combined model of all features using EQD2 surpassed all other models (AUC = 0.77, Confidence Interval CI 0.76-0.78). DVH, clinical data and ICI therapy had minor impact on PTP prediction with AUC values between 0.42 and 0.57. All EQD2-based models outperformed models based on physical dose. CONCLUSIONS: Radiomics + dosiomics based ML models combined with clinical and dosimetric models were found to be suited best for PTP prediction after R(C)T and could improve pre-treatment decision making. Different RT dose fractionation schemes should be considered for dose-based ML approaches.

A Deep Learning-Based Integrated Framework for Quality-Aware Undersampled Cine Cardiac MRI Reconstruction and Analysis.

Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this article, we present a fully-automated, quality-controlled integrated framework for reconstruction, segmentation and downstream analysis of undersampled cine CMR data. The framework produces high quality reconstructions and segmentations, leading to undersampling factors that are optimised on a scan-by-scan basis. This results in reduced scan times and automated analysis, enabling robust and accurate estimation of functional biomarkers. To demonstrate the feasibility of the proposed approach, we perform simulations of radial k-space acquisitions using in-vivo cine CMR data from 270 subjects from the UK Biobank (with synthetic phase) and in-vivo cine CMR data from 16 healthy subjects (with real phase). The results demonstrate that the optimal undersampling factor varies for different subjects by approximately 1 to 2 seconds per slice. We show that our method can produce quality-controlled images in a mean scan time reduced from 12 to 4 seconds per slice, and that image quality is sufficient to allow clinically relevant parameters to be automatically estimated to lie within 5% mean absolute difference.

Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review.

Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imaging artefacts. Deep learning has been frequently proposed for motion correction at several stages of the reconstruction process. The wide range of MR acquisition sequences, anatomies and pathologies of interest, and motion patterns (rigid vs. deformable and random vs. regular) makes a comprehensive solution unlikely. To facilitate the transfer of ideas between different applications, this review provides a detailed overview of proposed methods for learning-based motion correction in MRI together with their common challenges and potentials. This review identifies differences and synergies in underlying data usage, architectures, training and evaluation strategies. We critically discuss general trends and outline future directions, with the aim to enhance interaction between different application areas and research fields.

Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries.

Artificial intelligence (AI) is likely to revolutionize the way medical images are analysed and has the potential to improve the identification and analysis of vulnerable or high-risk atherosclerotic plaques in coronary arteries, leading to advances in the treatment of coronary artery disease. However, coronary plaque analysis is challenging owing to cardiac and respiratory motion, as well as the small size of cardiovascular structures. Moreover, the analysis of coronary imaging data is time-consuming, can be performed only by clinicians with dedicated cardiovascular imaging training, and is subject to considerable interreader and intrareader variability. AI has the potential to improve the assessment of images of vulnerable plaque in coronary arteries, but requires robust development, testing and validation. Combining human expertise with AI might facilitate the reliable and valid interpretation of images obtained using CT, MRI, PET, intravascular ultrasonography and optical coherence tomography. In this Roadmap, we review existing evidence on the application of AI to the imaging of vulnerable plaque in coronary arteries and provide consensus recommendations developed by an interdisciplinary group of experts on AI and non-invasive and invasive coronary imaging. We also outline future requirements of AI technology to address bias, uncertainty, explainability and generalizability, which are all essential for the acceptance of AI and its clinical utility in handling the anticipated growing volume of coronary imaging procedures.

Colorimetric Sensor Reading and Illumination Correction via Multi-Task Deep-Learning.

Colorimetric sensors represent an accessible and sensitive nanotechnology for rapid and accessible measurement of a substance's properties (e.g., analyte concentration) via color changes. Although colorimetric sensors are widely used in healthcare and laboratories, interpretation of their output is performed either by visual inspection or using cameras in highly controlled illumination set-ups, limiting their usage in end-user applications, with lower resolutions and altered light conditions. For that purpose, we implement a set of image processing and deep-learning (DL) methods that correct for non-uniform illumination alterations and accurately read the target variable from the color response of the sensor. Methods that perform both tasks independently vs. jointly in a multi-task model are evaluated. Video recordings of colorimetric sensors measuring temperature conditions were collected to build an experimental reference dataset. Sensor images were augmented with non-uniform color alterations. The best-performing DL architecture disentangles the luminance, chrominance, and noise via separate decoders and integrates a regression task in the latent space to predict the sensor readings, achieving a mean squared error (MSE) performance of 0.811±0.074[°C] and r2=0.930±0.007, under strong color perturbations, resulting in an improvement of 1.26[°C] when compared to the MSE of the best performing method with independent denoising and regression tasks.Clinical Relevance- The proposed methodology aims to improve the accuracy of colorimetric sensor reading and their large-scale accessibility as point-of-care diagnostic and continuous health monitoring devices, in altered illumination conditions.

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study.

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.

Engaging an HIV vaccine target through the acquisition of low B cell affinity.

Low affinity is common for germline B cell receptors (BCR) seeding development of broadly neutralizing antibodies (bnAbs) that engage hypervariable viruses, including HIV. Antibody affinity selection is also non-homogenizing, insuring the survival of low affinity B cell clones. To explore whether this provides a natural window for expanding human B cell lineages against conserved vaccine targets, we deploy transgenic mice mimicking human antibody diversity and somatic hypermutation (SHM) and immunize with simple monomeric HIV glycoprotein envelope immunogens. We report an immunization regimen that focuses B cell memory upon the conserved CD4 binding site (CD4bs) through both conventional affinity maturation and reproducible expansion of low affinity BCR clones with public patterns in SHM. In the latter instance, SHM facilitates target acquisition by decreasing binding strength. This suggests that permissive B cell selection enables the discovery of antibody epitopes, in this case an HIV bnAb site.

Targeting Solid Cancers with a Cancer-Specific Monoclonal Antibody to Surface Expressed Aberrantly O-glycosylated Proteins.

The lack of antibodies with sufficient cancer selectivity is currently limiting the treatment of solid tumors by immunotherapies. Most current immunotherapeutic targets are tumor-associated antigens that are also found in healthy tissues and often do not display sufficient cancer selectivity to be used as targets for potent antibody-based immunotherapeutic treatments, such as chimeric antigen receptor (CAR) T cells. Many solid tumors, however, display aberrant glycosylation that results in expression of tumor-associated carbohydrate antigens that are distinct from healthy tissues. Targeting aberrantly glycosylated glycopeptide epitopes within existing or novel glycoprotein targets may provide the cancer selectivity needed for immunotherapy of solid tumors. However, to date only a few such glycopeptide epitopes have been targeted. Here, we used O-glycoproteomics data from multiple cell lines to identify a glycopeptide epitope in CD44v6, a cancer-associated CD44 isoform, and developed a cancer-specific mAb, 4C8, through a glycopeptide immunization strategy. 4C8 selectively binds to Tn-glycosylated CD44v6 in a site-specific manner with low nanomolar affinity. 4C8 was shown to be highly cancer specific by IHC of sections from multiple healthy and cancerous tissues. 4C8 CAR T cells demonstrated target-specific cytotoxicity in vitro and significant tumor regression and increased survival in vivo. Importantly, 4C8 CAR T cells were able to selectively kill target cells in a mixed organotypic skin cancer model having abundant CD44v6 expression without affecting healthy keratinocytes, indicating tolerability and safety.

Fast fetal head compounding from multi-view 3D ultrasound.

The diagnostic value of ultrasound images may be limited by the presence of artefacts, notably acoustic shadows, lack of contrast and localised signal dropout. Some of these artefacts are dependent on probe orientation and scan technique, with each image giving a distinct, partial view of the imaged anatomy. In this work, we propose a novel method to fuse the partially imaged fetal head anatomy, acquired from numerous views, into a single coherent 3D volume of the full anatomy. Firstly, a stream of freehand 3D US images is acquired using a single probe, capturing as many different views of the head as possible. The imaged anatomy at each time-point is then independently aligned to a canonical pose using a recurrent spatial transformer network, making our approach robust to fast fetal and probe motion. Secondly, images are fused by averaging only the most consistent and salient features from all images, producing a more detailed compounding, while minimising artefacts. We evaluated our method quantitatively and qualitatively, using image quality metrics and expert ratings, yielding state of the art performance in terms of image quality and robustness to misalignments. Being online, fast and fully automated, our method shows promise for clinical use and deployment as a real-time tool in the fetal screening clinic, where it may enable unparallelled insight into the shape and structure of the face, skull and brain.

Extended reality for procedural planning and guidance in structural heart disease - a review of the state-of-the-art.

Extended reality (XR), which encompasses virtual, augmented and mixed reality, is an emerging medical imaging display platform which enables intuitive and immersive interaction in a three-dimensional space. This technology holds the potential to enhance understanding of complex spatial relationships when planning and guiding cardiac procedures in congenital and structural heart disease moving beyond conventional 2D and 3D image displays. A systematic review of the literature demonstrates a rapid increase in publications describing adoption of this technology. At least 33 XR systems have been described, with many demonstrating proof of concept, but with no specific mention of regulatory approval including some prospective studies. Validation remains limited, and true clinical benefit difficult to measure. This review describes and critically appraises the range of XR technologies and its applications for procedural planning and guidance in structural heart disease while discussing the challenges that need to be overcome in future studies to achieve safe and effective clinical adoption.

Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view.

Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation. In this work, we address these three challenges with a multi-task learning approach that combines the classification of placental location (e.g., anterior, posterior) and semantic placenta segmentation in a single convolutional neural network. Through the classification task the model can learn from larger and more diverse datasets while improving the accuracy of the segmentation task in particular in limited training set conditions. With this approach we investigate the variability in annotations from multiple raters and show that our automatic segmentations (Dice of 0.86 for anterior and 0.83 for posterior placentas) achieve human-level performance as compared to intra- and inter-observer variability. Lastly, our approach can deliver whole placenta segmentation using a multi-view US acquisition pipeline consisting of three stages: multi-probe image acquisition, image fusion and image segmentation. This results in high quality segmentation of larger structures such as the placenta in US with reduced image artifacts which are beyond the field-of-view of single probes.

Evaluation of a Linear Measurement Tool in Virtual Reality for Assessment of Multimodality Imaging Data-A Phantom Study.

This study aimed to evaluate the accuracy and reliability of a virtual reality (VR) system line measurement tool using phantom data across three cardiac imaging modalities: three-dimensional echocardiography (3DE), computed tomography (CT) and magnetic resonance imaging (MRI). The same phantoms were also measured using industry-standard image visualisation software packages. Two participants performed blinded measurements on volume-rendered images of standard phantoms both in VR and on an industry-standard image visualisation platform. The intra- and interrater reliability of the VR measurement method was evaluated by intraclass correlation coefficient (ICC) and coefficient of variance (CV). Measurement accuracy was analysed using Bland−Altman and mean absolute percentage error (MAPE). VR measurements showed good intra- and interobserver reliability (ICC ≥ 0.99, p < 0.05; CV < 10%) across all imaging modalities. MAPE for VR measurements compared to ground truth were 1.6%, 1.6% and 7.7% in MRI, CT and 3DE datasets, respectively. Bland−Altman analysis demonstrated no systematic measurement bias in CT or MRI data in VR compared to ground truth. A small bias toward smaller measurements in 3DE data was seen in both VR (mean −0.52 mm [−0.16 to −0.88]) and the standard platform (mean −0.22 mm [−0.03 to −0.40]) when compared to ground truth. Limits of agreement for measurements across all modalities were similar in VR and standard software. This study has shown good measurement accuracy and reliability of VR in CT and MRI data with a higher MAPE for 3DE data. This may relate to the overall smaller measurement dimensions within the 3DE phantom. Further evaluation is required of all modalities for assessment of measurements <10 mm.

Evolving polarisation of infiltrating and alveolar macrophages in the lung during metastatic progression of melanoma suggests CCR1 as a therapeutic target.

Metastatic tumour progression is facilitated by tumour associated macrophages (TAMs) that enforce pro-tumour mechanisms and suppress immunity. In pulmonary metastases, it is unclear whether TAMs comprise tissue resident or infiltrating, recruited macrophages; and the different expression patterns of these TAMs are not well established. Using the mouse melanoma B16F10 model of experimental pulmonary metastasis, we show that infiltrating macrophages (IM) change their gene expression from an early pro-inflammatory to a later tumour promoting profile as the lesions grow. In contrast, resident alveolar macrophages (AM) maintain expression of crucial pro-inflammatory/anti-tumour genes with time. During metastatic growth, the pool of macrophages, which initially contains mainly alveolar macrophages, increasingly consists of infiltrating macrophages potentially facilitating metastasis progression. Blocking chemokine receptor mediated macrophage infiltration in the lung revealed a prominent role for CCR2 in Ly6C+ pro-inflammatory monocyte/macrophage recruitment during metastasis progression, while inhibition of CCR2 signalling led to increased metastatic colony burden. CCR1 blockade, in contrast, suppressed late phase pro-tumour MR+Ly6C- monocyte/macrophage infiltration accompanied by expansion of the alveolar macrophage compartment and accumulation of NK cells, leading to reduced metastatic burden. These data indicate that IM has greater plasticity and higher phenotypic responsiveness to tumour challenge than AM. A considerable difference is also confirmed between CCR1 and CCR2 with regard to the recruited IM subsets, with CCR1 presenting a potential therapeutic target in pulmonary metastasis from melanoma.

Single-incision for breast-conserving surgery through round block technique.

AIMS AND OBJECTIVES: The purpose of this study was to assess the feasibility of using the single-incision round block technique in breast-conserving surgery with sentinel lymph node (SLN) retrieval for breast cancer without compromising oncological safety. MATERIALS AND METHODS: A retrospective observational case-control study was conducted from January 2017 to October 2021. The study population consisted of two groups. In both groups, breast-conserving surgery was carried out through the round-block technique. In group A, SLN retrieval was performed using the round-block incision (study group), while in group B, SLN retrieval was conducted through a second skin incision in the axilla (control group). The study was approved by the local ethics committee Zurich (BASEC-Nr. 2020-02857), and written informed consent was obtained from all participants. RESULTS: Overall, 134 patients met the inclusion criteria, of whom 86 women underwent breast-conserving surgery and SLN retrieval using the single-incision approach (group A), and 48 women underwent conventional surgery, using two independent incisions for tumour resection and SLN retrieval (group B). The overall success rate in group A regarding SLN retrieval was 97.7%, whereas most tumours were located in the upper outer (47.7%) and upper inner quadrant (27.9%). Although the technique was equally successful in the other quadrants, the share of tumours in the lower outer, and the lower inner quadrant, and the retroareolar region was smaller, representing 17.4%, 3.5% and 3.5%, respectively. The median number of dissected lymph nodes was two, with a positivity rate of 24.4%. The occurrence of axillary neuralgia and axillary skin retraction was significantly higher in group B along with tendentially more axillary seroma formation. There were no significant differences regarding reintervention rates, in terms of complications, resection margins, locoregional recurrences, or deaths with a mean follow-up of 11 months. CONCLUSIONS: The single-incision method through the round block technique is as safe and effective as the standard two-incision approach regarding nodal staging and resection margins, and seems to be applicable for tumours in all breast quadrants.

Memory-Efficient Training for Fully Unrolled Deep Learned PET Image Reconstruction with Iteration-Dependent Targets.

We propose a new version of the forward-backward splitting expectation-maximisation network (FBSEM-Net) along with a new memory-efficient training method enabling the training of fully unrolled implementations of 3D FBSEM-Net. FBSEM-Net unfolds the maximum a posteriori expectation-maximisation algorithm and replaces the regularisation step by a residual convolutional neural network. Both the gradient of the prior and the regularisation strength are learned from training data. In this new implementation, three modifications of the original framework are included. First, iteration-dependent networks are used to have a customised regularisation at each iteration. Second, iteration-dependent targets and losses are introduced so that the regularised reconstruction matches the reconstruction of noise-free data at every iteration. Third, sequential training is performed, making training of large unrolled networks far more memory efficient and feasible. Since sequential training permits unrolling a high number of iterations, there is no need for artificial use of the regularisation step as a leapfrogging acceleration. The results obtained on 2D and 3D simulated data show that FBSEM-Net using iteration-dependent targets and losses improves the consistency in the optimisation of the network parameters over different training runs. We also found that using iteration-dependent targets increases the generalisation capabilities of the network. Furthermore, unrolled networks using iteration-dependent regularisation allowed a slight reduction in reconstruction error compared to using a fixed regularisation network at each iteration. Finally, we demonstrate that sequential training successfully addresses potentially serious memory issues during the training of deep unrolled networks. In particular, it enables the training of 3D fully unrolled FBSEM-Net, not previously feasible, by reducing the memory usage by up to 98% compared to a conventional end-to-end training. We also note that the truncation of the backpropagation (due to sequential training) does not notably impact the network's performance compared to conventional training with a full backpropagation through the entire network.

Phage anti-CBASS and anti-Pycsar nucleases subvert bacterial immunity.

The cyclic oligonucleotide-based antiphage signalling system (CBASS) and the pyrimidine cyclase system for antiphage resistance (Pycsar) are antiphage defence systems in diverse bacteria that use cyclic nucleotide signals to induce cell death and prevent viral propagation1,2. Phages use several strategies to defeat host CRISPR and restriction-modification systems3-10, but no mechanisms are known to evade CBASS and Pycsar immunity. Here we show that phages encode anti-CBASS (Acb) and anti-Pycsar (Apyc) proteins that counteract defence by specifically degrading cyclic nucleotide signals that activate host immunity. Using a biochemical screen of 57 phages in Escherichia coli and Bacillus subtilis, we discover Acb1 from phage T4 and Apyc1 from phage SBSphiJ as founding members of distinct families of immune evasion proteins. Crystal structures of Acb1 in complex with 3'3'-cyclic GMP-AMP define a mechanism of metal-independent hydrolysis 3' of adenosine bases, enabling broad recognition and degradation of cyclic dinucleotide and trinucleotide CBASS signals. Structures of Apyc1 reveal a metal-dependent cyclic NMP phosphodiesterase that uses relaxed specificity to target Pycsar cyclic pyrimidine mononucleotide signals. We show that Acb1 and Apyc1 block downstream effector activation and protect from CBASS and Pycsar defence in vivo. Active Acb1 and Apyc1 enzymes are conserved in phylogenetically diverse phages, demonstrating that cleavage of host cyclic nucleotide signals is a key strategy of immune evasion in phage biology.

Improved 3D tumour definition and quantification of uptake in simulated lung tumours using deep learning.

Objective.In clinical positron emission tomography (PET) imaging, quantification of radiotracer uptake in tumours is often performed using semi-quantitative measurements such as the standardised uptake value (SUV). For small objects, the accuracy of SUV estimates is limited by the noise properties of PET images and the partial volume effect. There is need for methods that provide more accurate and reproducible quantification of radiotracer uptake.Approach.In this work, we present a deep learning approach with the aim of improving quantification of lung tumour radiotracer uptake and tumour shape definition. A set of simulated tumours, assigned with 'ground truth' radiotracer distributions, are used to generate realistic PET raw data which are then reconstructed into PET images. In this work, the ground truth images are generated by placing simulated tumours characterised by different sizes and activity distributions in the left lung of an anthropomorphic phantom. These images are then used as input to an analytical simulator to simulate realistic raw PET data. The PET images reconstructed from the simulated raw data and the corresponding ground truth images are used to train a 3D convolutional neural network.Results.When tested on an unseen set of reconstructed PET phantom images, the network yields improved estimates of the corresponding ground truth. The same network is then applied to reconstructed PET data generated with different point spread functions. Overall the network is able to recover better defined tumour shapes and improved estimates of tumour maximum and median activities.Significance.Our results suggest that the proposed approach, trained on data simulated with one scanner geometry, has the potential to restore PET data acquired with different scanners.

Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review.

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of LA scars provide important information on the pathophysiology and progression of atrial fibrillation (AF). Hence, LA LGE MRI computing and analysis are essential for computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineations can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched. This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar, and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies. Specifically, we first summarize AF-related imaging techniques, particularly LGE MRI. Then, we review the methodologies of the four computing tasks in detail and summarize the validation strategies applied in each task as well as state-of-the-art results on public datasets. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review indicates that the research into this topic is still in the early stages. Although several methods have been proposed, especially for the LA cavity segmentation, there is still a large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.

A Topological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology.

We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly provided and then incorporated into the training process. By using the differentiable properties of persistent homology, a concept used in topological data analysis, we can specify the desired topology of segmented objects in terms of their Betti numbers and then drive the proposed segmentations to contain the specified topological features. Importantly this process does not require any ground-truth labels, just prior knowledge of the topology of the structure being segmented. We demonstrate our approach in four experiments: one on MNIST image denoising and digit recognition, one on left ventricular myocardium segmentation from magnetic resonance imaging data from the UK Biobank, one on the ACDC public challenge dataset and one on placenta segmentation from 3-D ultrasound. We find that embedding explicit prior knowledge in neural network segmentation tasks is most beneficial when the segmentation task is especially challenging and that it can be used in either a semi-supervised or post-processing context to extract a useful training gradient from images without pixelwise labels.

Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time.

OBJECTIVE: Advances in artificial intelligence (AI) have demonstrated potential to improve medical diagnosis. We piloted the end-to-end automation of the mid-trimester screening ultrasound scan using AI-enabled tools. METHODS: A prospective method comparison study was conducted. Participants had both standard and AI-assisted US scans performed. The AI tools automated image acquisition, biometric measurement, and report production. A feedback survey captured the sonographers' perceptions of scanning. RESULTS: Twenty-three subjects were studied. The average time saving per scan was 7.62 min (34.7%) with the AI-assisted method (p < 0.0001). There was no difference in reporting time. There were no clinically significant differences in biometric measurements between the two methods. The AI tools saved a satisfactory view in 93% of the cases (four core views only), and 73% for the full 13 views, compared to 98% for both using the manual scan. Survey responses suggest that the AI tools helped sonographers to concentrate on image interpretation by removing disruptive tasks. CONCLUSION: Separating freehand scanning from image capture and measurement resulted in a faster scan and altered workflow. Removing repetitive tasks may allow more attention to be directed identifying fetal malformation. Further work is required to improve the image plane detection algorithm for use in real time.