Towards Automatic Cartilage Quantification in Clinical Trials - Continuing from the 2019 IWOAI Knee Segmentation Challenge.

Objective: To evaluate whether the deep learning (DL) segmentation methods from the six teams that participated in the IWOAI 2019 Knee Cartilage Segmentation Challenge are appropriate for quantifying cartilage loss in longitudinal clinical trials.

Design: We included 556 subjects from the Osteoarthritis Initiative study with manually read cartilage volume scores for the baseline and 1-year visits. The teams used their methods originally trained for the IWOAI 2019 challenge to segment the 1130 knee MRIs.

The Liver Tumor Segmentation Benchmark (LiTS).

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients.

Conventional MRI-derived subchondral trabecular biomarkers and their association with knee cartilage volume loss as early as 1 year: a longitudinal analysis from Osteoarthritis Initiative.

Objective: To study associations between MRI-derived subchondral trabecular biomarkers obtained from conventional MRI sequences and knee cartilage loss over 12 and 24 months, using the FNIH osteoarthritis (OA) biomarkers consortium.

Materials And Methods: Data of the 600 subjects in the FNIH OA biomarkers consortium (a nested case-control study within Osteoarthritis Initiative [OAI]) were extracted from the online database. Baseline knee MRI (intermediate-weighted (IW) sequences) were evaluated to determine conventional MRI-derived trabecular thickness (cTbTh) and bone-to-total ratio (cBV/TV).

Cross-Cohort Automatic Knee MRI Segmentation With Multi-Planar U-Nets.

Background: Segmentation of medical image volumes is a time-consuming manual task. Automatic tools are often tailored toward specific patient cohorts, and it is unclear how they behave in other clinical settings.

Purpose: To evaluate the performance of the open-source Multi-Planar U-Net (MPUnet), the validated Knee Imaging Quantification (KIQ) framework, and a state-of-the-art two-dimensional (2D) U-Net architecture on three clinical cohorts without extensive adaptation of the algorithms.

Fully automated quality control of rigid and affine registrations of T1w and T2w MRI in big data using machine learning.

Background: Magnetic resonance imaging (MRI)-based morphometry and relaxometry are proven methods for the structural assessment of the human brain in several neurological disorders. These procedures are generally based on T1-weighted (T1w) and/or T2-weighted (T2w) MRI scans, and rigid and affine registrations to a standard template(s) are essential steps in such studies. Therefore, a fully automatic quality control (QC) of these registrations is necessary in big data scenarios to ensure that they are suitable for subsequent processing.

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset.

Purpose: To organize a multi-institute knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.

Materials And Methods: A dataset partition consisting of three-dimensional knee MRI from 88 retrospective patients at two time points (baseline and 1-year follow-up) with ground truth articular (femoral, tibial, and patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated against ground truth segmentations using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a holdout test set.

Multivariable Modeling of Biomarker Data From the Phase I Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium.

Objective: To determine the optimal combination of imaging and biochemical biomarkers for use in the prediction of knee osteoarthritis (OA) progression.

Methods: The present study was a nested case-control trial from the Foundation of the National Institutes of Health OA Biomarkers Consortium that assessed study participants with a Kellgren/Lawrence grade of 1-3 who had complete biomarker data available (n = 539 to 550). Cases were participants' knees that had radiographic and pain progression between 24 and 48 months compared to baseline.

Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning.

Many diseases have no visual cues in the early stages, eluding image-based detection. Today, osteoarthritis (OA) is detected after bone damage has occurred, at an irreversible stage of the disease. Currently no reliable method exists for OA detection at a reversible stage.

Muscle fibre morphology and microarchitecture in cerebral palsy patients obtained by 3D synchrotron X-ray computed tomography.

Background: Synchrotron X-ray computed tomography (SXCT) allows for three-dimensional imaging of objects at a very high resolution and in large field-of-view.

Purpose: The aim of this study was to use SXCT imaging for morphological analysis of muscle tissue, in order to investigate whether the analysis reveals complementary information to two-dimensional microscopy.

Methods: Three-dimensional SXCT images of muscle biopsies were taken from participants with cerebral palsy and from healthy controls.

Gender Differences in Knee Joint Congruity Quantified from MRI: A Validation Study with Data from Center for Clinical and Basic Research and Osteoarthritis Initiative.

Objective Gender is a risk factor in the onset of osteoarthritis (OA). The aim of the study was to investigate gender differences in contact area (CA) and congruity index (CI) in the medial tibiofemoral (MTF) joint in 2 different cohorts, quantified automatically from magnetic resonance imaging (MRI). Design The CA and CI markers were validated on 2 different data sets from Center for Clinical and Basic Research (CCBR) and Osteoarthritis Initiative (OAI).

Prediction of medial tibiofemoral compartment joint space loss progression using volumetric cartilage measurements: Data from the FNIH OA biomarkers consortium.

Objectives: Investigating the association between baseline cartilage volume measurements (and initial 24th month volume loss) with medial compartment Joint-Space-Loss (JSL) progression (>0.7 mm) during 24-48th months of study.

Methods: Case and control cohorts (Biomarkers Consortium subset from the Osteoarthritis Initiative (OAI)) were defined as participants with (n=297) and without (n=303) medial JSL progression (during 24-48th months).

Deformation-based atrophy computation by surface propagation and its application to Alzheimer's disease.

Obtaining regional volume changes from a deformation field is more precise when using simplex counting (SC) compared with Jacobian integration (JI) due to the numerics involved in the latter. Although SC has been proposed before, numerical properties underpinning the method and a thorough evaluation of the method against JI is missing in the literature. The contributions of this paper are: (a) we propose surface propagation (SP)-a simplification to SC that significantly reduces its computational complexity; (b) we will derive the orders of approximation of SP which can also be extended to SC.

Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative.

Clinical studies including thousands of magnetic resonance imaging (MRI) scans offer potential for pathogenesis research in osteoarthritis. However, comprehensive quantification of all bone, cartilage, and meniscus compartments is challenging. We propose a segmentation framework for fully automatic segmentation of knee MRI.

Brain region's relative proximity as marker for Alzheimer's disease based on structural MRI.

Background: Alzheimer's disease (AD) is a progressive, incurable neurodegenerative disease and the most common type of dementia. It cannot be prevented, cured or drastically slowed, even though AD research has increased in the past 5-10 years. Instead of focusing on the brain volume or on the single brain structures like hippocampus, this paper investigates the relationship and proximity between regions in the brain and uses this information as a novel way of classifying normal control (NC), mild cognitive impaired (MCI), and AD subjects.

Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network.

Segmentation of anatomical structures in medical images is often based on a voxel/pixel classification approach. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images that fosters categorization. We propose a novel system for voxel classification integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D image, respectively.

Femoral cartilage segmentation in knee MRI scans using two stage voxel classification.

Using more than one classification stage and exploiting class population imbalance allows for incorporating powerful classifiers in tasks requiring large scale training data, even if these classifiers scale badly with the number of training samples. This led us to propose a two-stage classifier for segmenting tibial cartilage in knee MRI scans combining nearest neighbor classification and support vector machines (SVMs). Here we apply it to femoral cartilage segmentation.

Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps.

This study investigates whether measures of knee cartilage thickness can predict future loss of knee cartilage. A slow and a rapid progressor group was determined using longitudinal data, and anatomically aligned cartilage thickness maps were extracted from MRI at baseline. A novel machine learning framework was then trained using these maps.

On Subregional Analysis of Cartilage Loss from Knee MRI.

Objective: Understanding how knee cartilage is affected by osteoarthritis (OA) is critical in the development of sensitive biomarkers that may be used as surrogate endpoints in clinical trials. The objective of this study was to analyze longitudinal changes in cartilage thickness using detailed change maps and to examine if current methods for subregional analysis are able to capture the underlying cartilage changes.

Materials And Methods: MRI images of 267 knees from 135 participants were acquired at baseline and 21-month follow-up and processed using a fully automatic framework for cartilage segmentation and quantification.

Diagnosis of osteoarthritis and prognosis of tibial cartilage loss by quantification of tibia trabecular bone from MRI.

A longitudinal study was used to investigate the quantification of osteoarthritis and prediction of tibial cartilage loss by analysis of the tibia trabecular bone from magnetic resonance images of knees. The Kellgren Lawrence (KL) grades were determined by radiologists and the levels of cartilage loss were assessed by a segmentation process. Aiming to quantify and potentially capture the structure of the trabecular bone anatomy, a machine learning approach used a set of texture features for training a classifier to recognize the trabecular bone of a knee with radiographic osteoarthritis.

Distribution, size, and shape of abdominal aortic calcified deposits and their relationship to mortality in postmenopausal women.

Abdominal aortic calcifications (AACs) correlate strongly with coronary artery calcifications and can be predictors of cardiovascular mortality. We investigated whether size, shape, and distribution of AACs are related to mortality and how such prognostic markers perform compared to the state-of-the-art AC24 marker introduced by Kauppila. Methods.

Automatic analysis of trabecular bone structure from knee MRI.

We investigated the feasibility of quantifying osteoarthritis (OA) by analysis of the trabecular bone structure in low-field knee MRI. Generic texture features were extracted from the images and subsequently selected by sequential floating forward selection (SFFS), following a fully automatic, uncommitted machine-learning based framework. Six different classifiers were evaluated in cross-validation schemes and the results showed that the presence of OA can be quantified by a bone structure marker.

Automatic quantification of tibio-femoral contact area and congruity.

We present methods to quantify the medial tibio- femoral (MTF) joint contact area (CA) and congruity index (CI) from low-field magnetic resonance imaging (MRI). Firstly, based on the segmented MTF cartilage compartments, we computed the contact area using the Euclidian distance transformation. The CA was defined as the area of the tibial superior surface and the femoral inferior surface that are less than a voxel width apart.