2,408 research outputs found
Orientation-Sensitive Overlap Measures for the Validation of Medical Image Segmentations
Validation is a key concept in the development and assessment of medical image segmentation algorithms. However, the proliferation of modern, non-deterministic segmentation algorithms has not been met by an equivalent improvement in validation strategies. In this paper, we briefly examine the state of the art in validation, and propose an improved validation method for non-deterministic segmentations, showing that it improves validation precision and accuracy on both synthetic and clinical sets, compared to more traditional (but still widely used) methods and state of the art
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
Variable Scale Statistics For Cardiac Segmentation and Shape Analysis
A novel framework for medical image analysis, known as Shells and Spheres, has been developed by our research lab. This framework utilizes spherical operators of variable radius, centered at each image pixel and sized to reach, but not cross, the nearest boundary. Statistical population tests are performed on the populations of pixels within adjacent spheres to compare image regions across boundaries, delineating bothindependent image objects and the boundaries between them. This research has focused on developing the Shells and Spheres frameworkand applying it to the problem of segmentation of anatomical objects. Furthermore, we have rigorously studied the framework and its applications to clinical segmentation, validating and improving our n-dimensional segmentation algorithm. To this end, we have enhanced the original Shells and Spheres segmentation algorithm by adding a priori information, developing techniques for optimizing algorithm parameters, implementing a software platform for experimentation, and performing validation experiments using real 3D ovine cardiac MRI data. The system developed provides automated 3D segmentation given a priori information in the form of a trivial 2D manual training procedure, which involves tracing a single 2D contour from which 3D algorithm parameters are then automatically derived. We apply this system tosegmentation of the Right Ventricular Outflow Tract (RVOT) to aid in research toward the creation of a Tissue Engineered Pulmonary Valve(TEPV). Experimental methods are presented for the development and validation of the system, as well as a detailed description of the Shells and Spheres framework, our segmentation algorithm, and the clinical significance of this work
Vessel tractography using an intensity based tensor model with branch detection
In this paper, we present a tubular structure seg- mentation method that utilizes a second order tensor constructed from directional intensity measurements, which is inspired from diffusion tensor image (DTI) modeling. The constructed anisotropic tensor which is fit inside a vessel drives the segmen- tation analogously to a tractography approach in DTI. Our model is initialized at a single seed point and is capable of capturing whole vessel trees by an automatic branch detection algorithm developed in the same framework. The centerline of the vessel as well as its thickness is extracted. Performance results within the Rotterdam Coronary Artery Algorithm Evaluation framework are provided for comparison with existing techniques. 96.4% average overlap with ground truth delineated by experts is obtained in addition to other measures reported in the paper. Moreover, we demonstrate further quantitative results over synthetic vascular datasets, and we provide quantitative experiments for branch detection on patient Computed Tomography Angiography (CTA) volumes, as well as qualitative evaluations on the same CTA datasets, from visual scores by a cardiologist expert
Machine learning of hierarchical clustering to segment 2D and 3D images
We aim to improve segmentation through the use of machine learning tools
during region agglomeration. We propose an active learning approach for
performing hierarchical agglomerative segmentation from superpixels. Our method
combines multiple features at all scales of the agglomerative process, works
for data with an arbitrary number of dimensions, and scales to very large
datasets. We advocate the use of variation of information to measure
segmentation accuracy, particularly in 3D electron microscopy (EM) images of
neural tissue, and using this metric demonstrate an improvement over competing
algorithms in EM and natural images.Comment: 15 pages, 8 figure
Histological Quantification in Temporal Lobe Epilepsy
Approximately 30 percent of epilepsy patients suffer from refractory temporal lobe epilepsy which is commonly treated with resection of the epileptogenic tissue. However, surgical treatment presents many challenges in locating the epileptogenic focus and thus not all patients become seizure-free following surgery. Advances in techniques can lead to improved localization of the epileptogenic zone and may be validated by correlating MRI with neuropathology of the excised cortical tissue. Focal cortical dysplasias are a neuropathological group of cortical malformations that are often found in cases of refractory epilepsy, however, they are subtle and difficult to quantify. The purpose of this research is to employ histology image analysis techniques to better characterize these abnormalities at the neuronal and laminar level, allowing for correlative MRI-histology studies and improved lesion detection in medically intractable TLE
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