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. In the experiments, we will begin by empirically showing that SP is indeed nearly identical to SC, and that both methods are more stable than JI in presence of moderate to large deformation noise. Since SC and SP are identical, we consider SP as a representative of both the methods for a practical evaluation against JI. In a real application on Alzheimer’s disease neuroimaging initiative data, we show the following: (a) SP produces whole brain and medial temporal lobe atrophy numbers that are significantly better than JI at separating between normal controls and Alzheimer’s disease patients; (b) SP produces disease group atrophy differences comparable to or better than those obtained using FreeSurfer, demonstrating the validity of the obtained clinical results. Finally, in a reproducibility study, we show that the voxel-wise application of SP yields significantly lower variance when compared to JI.
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. The framework combines multiatlas rigid registration with voxel classification and was trained on manual segmentations with varying configurations of bones, cartilages, and menisci. The validation included high- and low-field knee MRI cohorts from the Center for Clinical and Basic Research, the osteoarthritis initiative (QAI), and the segmentation of knee images10 (SKI10) challenge. In total, 1907 knee MRIs were segmented during the evaluation. No segmentations were excluded. Our resulting OAI cartilage volume scores are available upon request. The precision and accuracy performances matched manual reader re-segmentation well. The cartilage volume scan-rescan precision was 4.9% (RMS CV). The Dice volume overlaps in the medial/lateral tibial/femoral cartilage compartments were 0.80 to 0.87. The correlations with volumes from independent methods were between 0.90 and 0.96 on the OAI scans. Thus, the framework demonstrated precision and accuracy comparable to manual segmentations. Finally, our method placed second for cartilage segmentation in the SKI10 challenge. The comprehensive validation suggested that automatic segmentation is appropriate for cohorts with thousands of scans.
This work investigates a novel way of looking at the regions in the brain and their relationship as possible markers to classify normal control (NC), mild cognitive impaired (MCI), and Alzheimer Disease (AD) subjects. MRI scans from a subset of 101 subjects from the ADNI study at baseline was used for this study. 40 regions in the brain including hippocampus, amygdala, thalamus, white, and gray matter were segmented using FreeSurfer. From this data, we calculated the distance between the center of mass of each region, the normalized number of voxels and the percentage volume and surface connectivity shared between the regions. These markers were used for classification using a linear discriminant analysis in a leave-one-out manner. We found that the percentage of surface and volume connectivity between regions gave a significant classification between NC and AD and borderline significant between MCI and AD even after correction for whole brain volume at baseline. The results show that the morphometric connectivity markers include more information than whole brain volume or distance markers. This suggests that one can gain additional information by combining morphometric connectivity markers with traditional volume and shape markers.
MRI-determined measurement of synovial inflammation (synovitis) from hand MRIs has recently gained
considerable popularity as a secondary marker in rheumatoid arthritis (RA) clinical trials. The currently
accepted scoring systems are, however, purely semi-quantitative and rely on assessment from a trained
radiologist. We propose a novel, fully automatic technique for quantitative wrist synovitis measurement
from two MRIs acquired before and after contrast agent injection. The technique estimates the volume of
the synovial inflammation in three steps. First, the wrist synovial membrane is segmented using multi-atlas
B-spline based freeform registration. Second, positioning differences between the pre- and post-contrast
acquisitions are corrected by rigid registration. Finally, wrist synovitis is quantified from the difference
between the pre- and post-contrast sequences in the region of the segmented synovium. We evaluate the
proposed technique on a data set of nineteen patients with acquisitions at two time points in a leave-one-patient-out fashion. Our experiments show that we are able to perform synovitis measurement with good
correlation to manual semi-quantitative RAMRIS scores for both static (r=0.84) and longitudinal (r=0.87)
scoring. These results compare favorably to the RAMRIS inter-observer variability.
KEYWORDS: Image segmentation, Cartilage, Magnetic resonance imaging, Medical imaging, Bone, Machine learning, Algorithm development, Scanners, 3D image processing, 3D modeling
Many classification/segmentation tasks in medical imaging are particularly challenging for machine learning algorithms
because of the huge amount of training data required to cover biological variability. Learning methods scaling badly in
the number of training data points may not be applicable. This may exclude powerful classifiers with good generalization
performance such as standard non-linear support vector machines (SVMs). Further, many medical imaging problems
have highly imbalanced class populations, because the object to be segmented has only few pixels/voxels compared to
the background. This article presents a two-stage classifier for large-scale medical imaging problems. In the first stage,
a classifier that is easily trainable on large data sets is employed. The class imbalance is exploited and the classifier is
adjusted to correctly detect background with a very high accuracy. Only the comparatively few data points not identified as
background are passed to the second stage. Here a powerful classifier with high training time complexity can be employed
for making the final decision whether a data point belongs to the object or not. We applied our method to the problem of
automatically segmenting tibial articular cartilage from knee MRI scans. We show that by using nearest neighbor (kNN)
in the first stage we can reduce the amount of data for training a non-linear SVM in the second stage. The cascaded system
achieves better results than the state-of-the-art method relying on a single kNN classifier.
Fully automatic imaging biomarkers may allow quantification of patho-physiological processes that a radiologist would
not be able to assess reliably. This can introduce new insight but is problematic to validate due to lack of meaningful
ground truth expert measurements. Rather than quantification accuracy, such novel markers must therefore be validated
against clinically meaningful end-goals such as the ability to allow correct diagnosis. We present a method for automatic
cartilage surface smoothness quantification in the knee joint. The quantification is based on a curvature flow method
used on tibial and femoral cartilage compartments resulting from an automatic segmentation scheme. These smoothness
estimates are validated for their ability to diagnose osteoarthritis and compared to smoothness estimates based on manual
expert segmentations and to conventional cartilage volume quantification. We demonstrate that the fully automatic
markers eliminate the time required for radiologist annotations, and in addition provide a diagnostic marker superior to
the evaluated semi-manual markers.
Osteoarthritis (OA) is a degenerative joint disease characterized by degradation of the articular cartilage, and is a
major cause of disability. At present, there is no cure for OA and currently available treatments are directed towards
relief of symptoms. Recently it was shown that cartilage homogeneity visualized by MRI and representing the
biochemical changes undergoing in the cartilage is a potential marker for early detection of knee OA. In this paper based
on homogeneity we present an automatic technique, embedded in a variational framework, for localization of a region of
interest in the knee cartilage that best indicates where the pathology of the disease is dominant. The technique is
evaluated on 283 knee MR scans. We show that OA affects certain areas of the cartilage more distinctly, and these are
more towards the peripheral region of the cartilage. We propose that this region in the cartilage corresponds anatomically
to the area covered by the meniscus in healthy subjects. This finding may provide valuable clues in the pathology and the
etiology of OA and thereby may improve treatment efficacy. Moreover our method is generic and may be applied to
other organs as well.
Osteoarthritis (OA) is a degenerative joint disease characterized by articular cartilage degradation. A central problem in
clinical trials is quantification of progression and early detection of the disease. The accepted standard for evaluating OA
progression is to measure the joint space width from radiographs however; there the cartilage is not visible. Recently
cartilage volume and thickness measures from MRI are becoming popular, but these measures don't account for the
biochemical changes undergoing in the cartilage before cartilage loss even occurs and therefore are not optimal for early
detection of OA. As a first step, we quantify cartilage homogeneity (computed as the entropy of the MR intensities) from
114 automatically segmented medial compartments of tibial cartilage sheets from Turbo 3D T1 sequences, from subjects
with no, mild or severe OA symptoms. We show that homogeneity is a more sensitive technique than volume
quantification for detecting early OA and for separating healthy individuals from diseased. During OA certain areas of
the cartilage are affected more and it is believed that these are the load-bearing regions located at the center of the
cartilage. Based on the homogeneity framework we present an automatic technique that partitions the region on the
cartilage that contributes to maximum homogeneity discrimination. These regions however, are more towards the noncentral
regions of the cartilage. Our observation will provide valuable clues to OA research and may lead to improving
treatment efficacy.
Osteo-Arthritis (OA) is a very common age-related cause of pain and reduced range of motion. A central effect of OA is wear-down of the articular cartilage that otherwise ensures smooth joint motion. Quantification of the cartilage breakdown is central in monitoring disease progression and therefore cartilage segmentation is required. Recent advances allow automatic cartilage segmentation with high accuracy in most cases. However, the automatic methods still fail in some problematic cases. For clinical studies, even if a few failing cases will be averaged out in the overall results, this reduces the mean accuracy and precision and thereby necessitates larger/longer studies. Since the severe OA cases are often most problematic for the automatic methods, there is even a risk that the quantification will introduce a bias in the results. Therefore, interactive inspection and correction of these problematic cases is desirable. For diagnosis on individuals, this is even more crucial since the diagnosis will otherwise simply fail.
We introduce and evaluate a semi-automatic cartilage segmentation method combining an automatic pre-segmentation with an interactive step that allows inspection and correction. The automatic step consists of voxel classification based on supervised learning. The interactive step combines a watershed transformation of the original scan with the posterior probability map from the classification step at sub-voxel precision. We evaluate the method for the task of segmenting the tibial cartilage sheet from low-field magnetic resonance imaging (MRI) of knees. The evaluation shows that the combined method allows accurate and highly reproducible correction of the segmentation of even the worst cases in approximately ten minutes of interaction.
KEYWORDS: Cartilage, Image segmentation, Magnetic resonance imaging, 3D image processing, Image processing, Image classification, 3D scanning, Basic research, Machine learning, Drug development
Accurate computation of the thickness of the articular cartilage is
of great importance when diagnosing and monitoring the progress of
joint diseases such as osteoarthritis. A fully automated cartilage
assessment method is preferable compared to methods using manual
interaction in order to avoid inter- and intra-observer variability.
As a first step in the cartilage assessment, we present an automatic
method for locating articular cartilage in knee MRI using supervised
learning. The next step will be to fit a variable shape model to the
cartilage, initiated at the location found using the method
presented in this paper. From the model, disease markers will be
extracted for the quantitative evaluation of the cartilage. The
cartilage is located using an ANN-classifier, where every voxel is
classified as cartilage or non-cartilage based on prior knowledge of
the cartilage structure. The classifier is tested using leave-one-out-evaluation, and we found the average sensitivity and specificity to be 91.0% and 99.4%, respectively. The center of mass calculated from voxels classified as cartilage are similar to the corresponding values calculated from manual segmentations, which confirms that this method can find a good initial position for a shape model.
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