85 research outputs found
Deep Learning for Musculoskeletal Image Analysis
The diagnosis, prognosis, and treatment of patients with musculoskeletal
(MSK) disorders require radiology imaging (using computed tomography, magnetic
resonance imaging(MRI), and ultrasound) and their precise analysis by expert
radiologists. Radiology scans can also help assessment of metabolic health,
aging, and diabetes. This study presents how machinelearning, specifically deep
learning methods, can be used for rapidand accurate image analysis of MRI
scans, an unmet clinicalneed in MSK radiology. As a challenging example, we
focus on automatic analysis of knee images from MRI scans and study machine
learning classification of various abnormalities including meniscus and
anterior cruciate ligament tears. Using widely used convolutional neural
network (CNN) based architectures, we comparatively evaluated the knee
abnormality classification performances of different neural network
architectures under limited imaging data regime and compared single and
multi-view imaging when classifying the abnormalities. Promising results
indicated the potential use of multi-view deep learning based classification of
MSK abnormalities in routine clinical assessment.Comment: Invited Paper, ASILOMAR 2019, TP4b: Machine Learning Advances in
Computational Imagin
Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI
Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue
(IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is
useful for clinical and research investigations in various conditions such as
aging, diabetes mellitus, obesity, metabolic syndrome, and their associated
comorbidities. Towards a fully automated, robust, and precise quantification of
thigh tissues, herein we designed a novel semi-supervised segmentation
algorithm based on deep network architectures. Built upon Tiramisu segmentation
engine, our proposed deep networks use variational and specially designed
targeted dropouts for faster and robust convergence, and utilize multi-contrast
MRI scans as input data. In our experiments, we have used 150 scans from 50
distinct subjects from the Baltimore Longitudinal Study of Aging (BLSA). The
proposed system made use of both labeled and unlabeled data with high efficacy
for training, and outperformed the current state-of-the-art methods with dice
scores of 97.52%, 94.61%, 80.14%, 95.93%, and 96.83% for muscle, fat, IMAT,
bone, and bone marrow tissues, respectively. Our results indicate that the
proposed system can be useful for clinical research studies where volumetric
and distributional tissue quantification is pivotal and labeling is a
significant issue. To the best of our knowledge, the proposed system is the
first attempt at multi-tissue segmentation using a single end-to-end
semi-supervised deep learning framework for multi-contrast thigh MRI scans.Comment: 20 pages, 9 figures, Journal of Signal Processing System
Recommended from our members
Chest Fat Quantification via CT Based on Standardized Anatomy Space in Adult Lung Transplant Candidates
Purpose
Overweight and underweight conditions are considered relative contraindications to lung transplantation due to their association with excess mortality. Yet, recent work suggests that body mass index (BMI) does not accurately reflect adipose tissue mass in adults with advanced lung diseases. Alternative and more accurate measures of adiposity are needed. Chest fat estimation by routine computed tomography (CT) imaging may therefore be important for identifying high-risk lung transplant candidates. In this paper, an approach to chest fat quantification and quality assessment based on a recently formulated concept of standardized anatomic space (SAS) is presented. The goal of the paper is to seek answers to several key questions related to chest fat quantity and quality assessment based on a single slice CT (whether in the chest, abdomen, or thigh) versus a volumetric CT, which have not been addressed in the literature.
Methods
Unenhanced chest CT image data sets from 40 adult lung transplant candidates (age 58 ± 12 yrs and BMI 26.4 ± 4.3 kg/m2), 16 with chronic obstructive pulmonary disease (COPD), 16 with idiopathic pulmonary fibrosis (IPF), and the remainder with other conditions were analyzed together with a single slice acquired for each patient at the L5 vertebral level and mid-thigh level. The thoracic body region and the interface between subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in the chest were consistently defined in all patients and delineated using Live Wire tools. The SAT and VAT components of chest were then segmented guided by this interface. The SAS approach was used to identify the corresponding anatomic slices in each chest CT study, and SAT and VAT areas in each slice as well as their whole volumes were quantified. Similarly, the SAT and VAT components were segmented in the abdomen and thigh slices. Key parameters of the attenuation (Hounsfield unit (HU) distributions) were determined from each chest slice and from the whole chest volume separately for SAT and VAT components. The same parameters were also computed from the single abdominal and thigh slices. The ability of the slice at each anatomic location in the chest (and abdomen and thigh) to act as a marker of the measures derived from the whole chest volume was assessed via Pearson correlation coefficient (PCC) analysis.
Results
The SAS approach correctly identified slice locations in different subjects in terms of vertebral levels. PCC between chest fat volume and chest slice fat area was maximal at the T8 level for SAT (0.97) and at the T7 level for VAT (0.86), and was modest between chest fat volume and abdominal slice fat area for SAT and VAT (0.73 and 0.75, respectively). However, correlation was weak for chest fat volume and thigh slice fat area for SAT and VAT (0.52 and 0.37, respectively), and for chest fat volume for SAT and VAT and BMI (0.65 and 0.28, respectively). These same single slice locations with maximal PCC were found for SAT and VAT within both COPD and IPF groups. Most of the attenuation properties derived from the whole chest volume and single best chest slice for VAT (but not for SAT) were significantly different between COPD and IPF groups.
Conclusions
This study demonstrates a new way of optimally selecting slices whose measurements may be used as markers of similar measurements made on the whole chest volume. The results suggest that one or two slices imaged at T7 and T8 vertebral levels may be enough to estimate reliably the total SAT and VAT components of chest fat and the quality of chest fat as determined by attenuation distributions in the entire chest volume
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 knee MRI segmentation challenge for characterizing the
semantic and clinical efficacy of automatic segmentation methods relevant for
monitoring osteoarthritis progression.
Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at
two timepoints with ground-truth articular (femoral, tibial, patellar)
cartilage and meniscus segmentations was standardized. Challenge submissions
and a majority-vote ensemble were evaluated using Dice score, average symmetric
surface distance, volumetric overlap error, and coefficient of variation on a
hold-out test set. Similarities in network segmentations were evaluated using
pairwise Dice correlations. Articular cartilage thickness was computed per-scan
and longitudinally. Correlation between thickness error and segmentation
metrics was measured using Pearson's coefficient. Two empirical upper bounds
for ensemble performance were computed using combinations of model outputs that
consolidated true positives and true negatives.
Results: Six teams (T1-T6) submitted entries for the challenge. No
significant differences were observed across all segmentation metrics for all
tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice
correlations between network pairs were high (>0.85). Per-scan thickness errors
were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal
bias (<0.03mm). Low correlations (<0.41) were observed between segmentation
metrics and thickness error. The majority-vote ensemble was comparable to top
performing networks (p=1.0). Empirical upper bound performances were similar
for both combinations (p=1.0).
Conclusion: Diverse networks learned to segment the knee similarly where high
segmentation accuracy did not correlate to cartilage thickness accuracy. Voting
ensembles did not outperform individual networks but may help regularize
individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo
Uncommon Causes of Interlobular Septal Thickening on CT Images and Their Distinguishing Features
Interlobular septa thickening (ILST) is a common and easily recognized feature on computed tomography (CT) images in many lung disorders. ILST thickening can be smooth (most common), nodular, or irregular. Smooth ILST can be seen in pulmonary edema, pulmonary alveolar proteinosis, and lymphangitic spread of tumors. Nodular ILST can be seen in the lymphangitic spread of tumors, sarcoidosis, and silicosis. Irregular ILST is a finding suggestive of interstitial fibrosis, which is a common finding in fibrotic lung diseases, including sarcoidosis and usual interstitial pneumonia. Pulmonary edema and lymphangitic spread of tumors are the commonly encountered causes of ILST. It is important to narrow down the differential diagnosis as much as possible by assessing the appearance and distribution of ILST, as well as other pulmonary and extrapulmonary findings. This review will focus on the CT characterization of the secondary pulmonary lobule and ILST. Various uncommon causes of ILST will be discussed, including infections, interstitial pneumonia, depositional/infiltrative conditions, inhalational disorders, malignancies, congenital/inherited conditions, and iatrogenic causes. Awareness of the imaging appearance and various causes of ILST allows for a systematic approach, which is important for a timely diagnosis. This study highlights the importance of a structured approach to CT scan analysis that considers ILST characteristics, associated findings, and differential diagnostic considerations to facilitate accurate diagnoses
Multiple fuzzy object modeling improves sensitivity in automatic anatomy recognition
Computerized automatic anatomy recognition (AAR) is an essential step for implementing body-wide
quantitative radiology (QR). Our strategy to automatically identify and delineate various organs in a given body region is based on fuzzy models and an organ hierarchy. In previous years, the basic algorithms of our AAR approach - model building, recognition, and delineation - and their evaluation were presented. In the present paper, we propose to replace the single fuzzy model built for each organ by a set of fuzzy models built for the same organ. Based on a dataset composed of CT images of the Thorax region of 50 subjects, our experiments indicate that recognition performance improves when using multiple models instead of a single model for each organ. It is interesting to point out that the improvement is not uniform for all organs, leading us to conclude that some organs will benefit from the multiple model approach more than others9034CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPsem informaçãoMedical Imaging 2014: Image Processin
- …