Automated quantification of lung pathology on micro-CT in diverse disease models using deep learning.

Background: Micro-CT significantly enhances the efficiency, predictive power and translatability of animal studies to human clinical trials for respiratory diseases. However, the analysis of large micro-CT datasets remains a bottleneck.

Methods: We developed a generic deep learning (DL)-based lung segmentation model using longitudinal micro-CT images from studies of Down syndrome, viral and fungal infections, and exacerbation with variable lung pathology and degree of disease burden.

Local response function estimation in spherical deconvolution for comprehensive tissue representation using diffusion MRI.

Diffusion MRI (dMRI) plays a crucial role in studying tissue microstructure and fibre orientation. Due to the intricate nature of the dMRI signal, end users require representations that provide a straightforward interpretation. Currently, these representations rely on tissue-average estimations or simplified tissue models and are hence limited in their applicability to pathology.

Real-time automated assessment of histological disease activity in patients with ulcerative colitis using single-wavelength endoscopy technology.

Single-wavelength endoscopy (SWE) has shown promising results in assessing histological disease activity in ulcerative colitis. Our objective was to validate the real-time performance of a bedside prototype of SWE computer-aided diagnosis (CAD) as proof of concept.A bedside module for real-time use evaluated histological disease activity when endoscopy was performed in the rectum and sigmoid based on white-light endoscopy and SWE (410 nm monochromatic light).

Development of an Automated Tool for the Estimation of Histological Remission in Ulcerative Colitis Using Single-Wavelength Endoscopy Technology.

Background And Aims: Ulcerative colitis (UC) management employs a strategy targeting histological and endoscopic remission. Correlation of white light endoscopy (WLE) scores with histological activity is limited. Single-wavelength endoscopy (SWE), addressing microvascular changes reflecting histological disease activity, may better assess histological remission.

Segmentation-based quantitative measurements in renal CT imaging using deep learning.

Background: Renal quantitative measurements are important descriptors for assessing kidney function. We developed a deep learning-based method for automated kidney measurements from computed tomography (CT) images.

Methods: The study datasets comprised potential kidney donors (n = 88), both contrast-enhanced (Dataset 1 CE) and noncontrast (Dataset 1 NC) CT scans, and test sets of contrast-enhanced cases (Test set 2, n = 18), cases from a photon-counting (PC)CT scanner reconstructed at 60 and 190 keV (Test set 3 PCCT, n = 15), and low-dose cases (Test set 4, n = 8), which were retrospectively analyzed to train, validate, and test two networks for kidney segmentation and subsequent measurements.

Automated biventricular quantification in patients with repaired tetralogy of Fallot using a three-dimensional deep learning segmentation model.

Background: Deep learning is the state-of-the-art approach for automated segmentation of the left ventricle (LV) and right ventricle (RV) in cardiovascular magnetic resonance (CMR) images. However, these models have been mostly trained and validated using CMR datasets of structurally normal hearts or cases with acquired cardiac disease, and are therefore not well-suited to handle cases with congenital cardiac disease such as tetralogy of Fallot (TOF). We aimed to develop and validate a dedicated model with improved performance for LV and RV cavity and myocardium quantification in patients with repaired TOF.

Test-retest reliability and follow-up of muscle magnetic resonance elastography in adults with and without muscle diseases.

Background: We investigated the potential of magnetic resonance elastography (MRE) stiffness measurements in skeletal muscles as an outcome measure, by determining its test-retest reliability, as well as its sensitivity to change in a longitudinal follow-up study.

Methods: We assessed test-retest reliability of muscle MRE in 20 subjects with (n = 5) and without (n = 15) muscle diseases and compared this to Dixon proton density fat fraction (PDFF) and volume measurements. Next, we measured MRE muscle stiffness in 21 adults with Becker muscular dystrophy (BMD) and 21 age-matched healthy controls at baseline, and after 9 and 18 months.

Clinical consequences of computer-aided colorectal polyp detection.

Background And Aim: Randomised trials show improved polyp detection with computer-aided detection (CADe), mostly of small lesions. However, operator and selection bias may affect CADe's true benefit. Clinical outcomes of increased detection have not yet been fully elucidated.

Lessons for future clinical trials in adults with Becker muscular dystrophy: Disease progression detected by muscle magnetic resonance imaging, clinical and patient-reported outcome measures.

Background And Purpose: Because Becker muscular dystrophy (BMD) is a heterogeneous disease and only few studies have evaluated adult patients, it is currently still unclear which outcome measures should be used in future clinical trials.

Methods: Muscle magnetic resonance imaging, patient-reported outcome measures and a wide range of clinical outcome measures, including motor function, muscle strength and timed-function tests, were evaluated in 21 adults with BMD at baseline and at 9 and 18 months of follow-up.

Results: Proton density fat fraction increased significantly in 10/17 thigh muscles after 9 months, and in all thigh and lower leg muscles after 18 months.

Deep learning pipeline for quality filtering of MRSI spectra.

With the rise of novel 3D magnetic resonance spectroscopy imaging (MRSI) acquisition protocols in clinical practice, which are capable of capturing a large number of spectra from a subject's brain, there is a need for an automated preprocessing pipeline that filters out bad-quality spectra and identifies contaminated but salvageable spectra prior to the metabolite quantification step. This work introduces such a pipeline based on an ensemble of deep-learning classifiers. The dataset consists of 36,338 spectra from one healthy subject and five brain tumor patients, acquired with an EPSI variant, which implemented a novel type of spectral editing named SLOtboom-Weng (SLOW) editing on a 7T MR scanner.

Advanced Imaging in Gastrointestinal Endoscopy: A Literature Review of the Current State of the Art.

Background And Aims: Gastrointestinal (GI) endoscopy has known a great evolution in the last decades. Imaging techniques evolved from imaging with only standard white light endoscopes toward high-definition resolution endoscopes and the use of multiple color enhancement techniques, over to automated endoscopic assessment systems based on artificial intelligence. This narrative literature review aimed to provide a detailed overview on the latest evolutions within the field of advanced GI endoscopy, mainly focusing on the screening, diagnosis, and surveillance of common upper and lower GI pathology.

Automated MRI quantification of volumetric per-muscle fat fractions in the proximal leg of patients with muscular dystrophies.

Muscular dystrophies (MD) are a class of rare genetic diseases resulting in progressive muscle weakness affecting specific muscle groups, depending on the type of disease. Disease progression is characterized by the gradual replacement of muscle tissue by fat, which can be assessed with fat-sensitive magnetic resonance imaging (MRI) and objectively evaluated by quantifying the fat fraction percentage (FF%) per muscle. Volumetric quantification of fat replacement over the full 3D extent of each muscle is more precise and potentially more sensitive than 2D quantification in few selected slices only, but it requires an accurate 3D segmentation of each muscle individually, which is time consuming when this has to be performed manually for a large number of muscles.

Histopathological correlations and fat replacement imaging patterns in recessive limb-girdle muscular dystrophy type 12.

Background: Despite the widespread use of proton density fat fraction (PDFF) measurements with magnetic resonance imaging (MRI) to track disease progression in muscle disorders, it is still unclear how these findings relate to histopathological changes in muscle biopsies of patients with limb-girdle muscular dystrophy autosomal recessive type 12 (LGMDR12). Furthermore, although it is known that LGMDR12 leads to a selective muscle involvement distinct from other muscular dystrophies, the spatial distribution of fat replacement within these muscles is unknown.

Methods: We included 27 adult patients with LGMDR12 and 27 age-matched and sex-matched healthy controls and acquired 6-point Dixon images of the thighs and T1 and short tau inversion recovery (STIR) MR images of the whole body.

Benefits of automated gross tumor volume segmentation in head and neck cancer using multi-modality information.

Purpose: Gross tumor volume (GTV) delineation for head and neck cancer (HNC) radiation therapy planning is time consuming and prone to interobserver variability (IOV). The aim of this study was (1) to develop an automated GTV delineation approach of primary tumor (GTVp) and pathologic lymph nodes (GTVn) based on a 3D convolutional neural network (CNN) exploiting multi-modality imaging input as required in clinical practice, and (2) to validate its accuracy, efficiency and IOV compared to manual delineation in a clinical setting.

Methods: Two datasets were retrospectively collected from 150 clinical cases.

Factorizer: A scalable interpretable approach to context modeling for medical image segmentation.

Convolutional Neural Networks (CNNs) with U-shaped architectures have dominated medical image segmentation, which is crucial for various clinical purposes. However, the inherent locality of convolution makes CNNs fail to fully exploit global context, essential for better recognition of some structures, e.g.

Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome.

The main goal of this study is to investigate the discrimination power of Grey Matter (GM) thickness connectome data between Multiple Sclerosis (MS) clinical profiles using statistical and Machine Learning (ML) methods. A dataset composed of 90 MS patients acquired at the MS clinic of Lyon Neurological Hospital was used for the analysis. Four MS profiles were considered, corresponding to Clinical Isolated Syndrome (CIS), Relapsing-Remitting MS (RRMS), Secondary Progressive MS (SPMS), and Primary Progressive MS (PPMS).

Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI.

Semantic segmentation using convolutional neural networks (CNNs) is the state-of-the-art for many medical image segmentation tasks including myocardial segmentation in cardiac MR images. However, the predicted segmentation maps obtained from such standard CNN do not allow direct quantification of regional shape properties such as regional wall thickness. Furthermore, the CNNs lack explicit shape constraints, occasionally resulting in unrealistic segmentations.

Treatment plan prediction for lung IMRT using deep learning based fluence map generation.

Purpose: Recently, it has been shown that automated treatment planning can be executed by direct fluence prediction from patient anatomy using convolutional neural networks. Proof of principle publications utilise a fixed dose prescription and fixed collimator (0°) and gantry angles. The goal of this work is to further develop these principles for the challenging lung cancer indication with variable dose prescriptions, collimator and gantry angles.

Prospective Natural History Study in 24 Adult Patients With LGMDR12 Over 2 Years of Follow-up: Quantitative MRI and Clinical Outcome Measures.

Background And Objectives: Limb-girdle muscular dystrophy autosomal recessive type 12 (LGMDR12) is a rare hereditary muscular dystrophy for which outcome measures are currently lacking. We evaluated quantitative MRI and clinical outcome measures to track disease progression to determine which tests could be useful in future clinical trials to evaluate potential therapies.

Methods: We prospectively measured the following outcome measures in all participants at baseline and after 1 and 2 years: 6-minute walk distance (6MWD), 10-meter walk test (10MWT), the Medical Research Council (MRC) sum scores, Biodex isometric dynamometry, serum creatine kinase, and 6-point Dixon MRI of the thighs.

Corrigendum: Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty.

[This corrects the article DOI: 10.3389/frobt.2022.

Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty.

Previous studies have shown that the manufacturer's default preoperative plans for total knee arthroplasty with patient-specific guides require frequent, time-consuming changes by the surgeon. Currently, no research has been done on predicting preoperative plans for orthopedic surgery using machine learning. Therefore, this study aims to evaluate whether artificial intelligence (AI) driven planning tools can create surgeon and patient-specific preoperative plans that require fewer changes by the surgeon.

Cross-Modal Distillation to Improve MRI-Based Brain Tumor Segmentation With Missing MRI Sequences.

Convolutional neural networks (CNNs) for brain tumor segmentation are generally developed using complete sets of magnetic resonance imaging (MRI) sequences for both training and inference. As such, these algorithms are not trained for realistic, clinical scenarios where parts of the MRI sequences which were used for training, are missing during inference. To increase clinical applicability, we proposed a cross-modal distillation approach to leverage the availability of multi-sequence MRI data for training and generate an enriched CNN model which uses only single-sequence MRI data for inference but outperforms a single-sequence CNN model.

Pitfalls in training and validation of deep learning systems.

The number of publications in endoscopic journals that present deep learning applications has risen tremendously over the past years. Deep learning has shown great promise for automated detection, diagnosis and quality improvement in endoscopy. However, the interdisciplinary nature of these works has undoubtedly made it more difficult to estimate their value and applicability.

icobrain ms 5.1: Combining unsupervised and supervised approaches for improving the detection of multiple sclerosis lesions.

Multiple sclerosis (MS) is a chronic autoimmune, inflammatory neurological disease of the central nervous system. Its diagnosis nowadays commonly includes performing an MRI scan, as it is the most sensitive imaging test for MS. MS plaques are commonly identified from fluid-attenuated inversion recovery (FLAIR) images as hyperintense regions that are highly varying in terms of their shapes, sizes and locations, and are routinely classified in accordance to the McDonald criteria.

Ga-PSMA-11 PET, F-PSMA-1007 PET, and MRI for Gross Tumor Volume Delineation in Primary Prostate Cancer: Intermodality and Intertracer Variability.

Purpose: To assess the intermodality and intertracer variability of gallium-68 (Ga)- or fluorine-18 (F)-labeled prostate-specific membrane antigen (PSMA) positron emission tomography (PET) and biparametric magnetic resonance imaging (bpMRI)-based gross tumor volume (GTV) delineation for focal boosting in primary prostate cancer.

Methods: Nineteen prospectively enrolled patients with prostate cancer underwent a PSMA PET/MRI scan, divided into a 1:1 ratio between Ga-PSMA-11 and F-PSMA-1007, before radical prostatectomy (IWT140193). Four delineation teams performed manual contouring of the GTV based on bpMRI and PSMA PET imaging, separately.