Purpose: Positron Emission Tomography (PET) is an advanced clinical imaging modality within the field of nuclear medicine. Despite strict adherence to standardized imaging protocols, variations in image indices caused by various factors complicate the comparison of multicenter PET data. This study aims to investigate whether a Gamma model-based standardized uptake value (SUV) harmonization algorithm can improve the classification of imaging radiomics features across multicenter datasets. Methods: This study includes PET/CT and PET/MR image data of 251 patients from Guangdong Provincial People's Hospital without significant lesions in the liver, spleen, and bone regions. A Gamma modelbased harmonization algorithm is developed and evaluated for its impact on radiomic-based tissue classification. The effectiveness of the harmonization algorithm is assessed based on classification accuracy, in comparison with unharmonized and ComBat-harmonized data. Results: The study identifies 86 radiomic features spanning five categories from volumes of interest (VOIs) in three tissues. Our harmonization algorithm significantly improved tissue classification accuracy compared to unharmonized data. By utilizing the PCA-reduced 10-dimensional radiomic features, the algorithm achieved a mean accuracy of 94.5% and 93.0% in the training and validation sets, respectively, representing a substantial improvement over the unharmonized accuracies of 55.8% and 52.0%. Conclusion: These findings suggest that the Gamma model-based harmonization algorithm facilitates the investigation of imaging radiomics in multicenter data.
Shown in high resolution images, a morphological feature that can be clearly observed is the bumpy ridges on the inferior aspect of hippocampus, which we refer to as hippocampal dentation. The dentations of the hippocampus in normal individuals vary greatly from highly smooth to highly dentated. The degree of dentation could be an interesting feature which has been shown to be correlated with episodic memory performance and not to be correlated with hippocampal volume. Here we presented a study which quantitatively evaluated the degree of bumpiness under the hippocampi in 552 healthy subjects with the age of mid-20 to 80. Specifically, the principal component analysis (PCA) which is nonlinearly fitted for quantifying the magnitude and the frequency of the hippocampal dentations has been used to identify the major axes of the hippocampus and the dentations under it. Preliminary results have demonstrated that the level of dentations varies between left and right hippocampi in subjects, as well as across different age groups. This can establish an objective and quantitative measurement for such a feature and can be extended for future comparisons between non-clinical and clinical groups.
This paper presents a fully automatic approach to grade intermediate prostate malignancy with hematoxylin and eosin-stained whole slide images. Deep learning architectures such as convolutional neural networks have been utilized in the domain of histopathology for automated carcinoma detection and classification. However, few work show its power in discriminating intermediate Gleason patterns, due to sporadic distribution of prostate glands on stained surgical section samples. We propose optimized hematoxylin decomposition on localized images, followed by convolutional neural network to classify Gleason patterns 3+4 and 4+3 without handcrafted features or gland segmentation. Crucial glands morphology and structural relationship of nuclei are extracted twice in different color space by the multi-scale strategy to mimic pathologists’ visual examination. Our novel classification scheme evaluated on 169 whole slide images yielded a 70.41% accuracy and corresponding area under the receiver operating characteristic curve of 0.7247.
Digital histopathology images with more than 1 Gigapixel are drawing more and more attention in clinical,
biomedical research, and computer vision fields. Among the multiple observable features spanning multiple
scales in the pathology images, the nuclear morphology is one of the central criteria for diagnosis and grading.
As a result it is also the mostly studied target in image computing. Large amount of research papers have
devoted to the problem of extracting nuclei from digital pathology images, which is the foundation of any
further correlation study. However, the validation and evaluation of nucleus extraction have yet been formulated
rigorously and systematically. Some researches report a human verified segmentation with thousands of nuclei,
whereas a single whole slide image may contain up to million. The main obstacle lies in the difficulty of obtaining
such a large number of validated nuclei, which is essentially an impossible task for pathologist. We propose a
systematic validation and evaluation approach based on large scale image synthesis. This could facilitate a more
quantitatively validated study for current and future histopathology image analysis field.
Digital histopathological images provide detailed spatial information of the tissue at micrometer resolution. Among the available contents in the pathology images, meso-scale information, such as the gland morphology, texture, and distribution, are useful diagnostic features. In this work, focusing on the colon-rectal cancer tissue samples, we propose a multi-scale learning based segmentation scheme for the glands in the colon-rectal digital pathology slides. The algorithm learns the gland and non-gland textures from a set of training images in various scales through a sparse dictionary representation. After the learning step, the dictionaries are used collectively to perform the classification and segmentation for the new image.
Extracting nuclei is one of the most actively studied topic in the digital pathology researches. Most of the studies directly search the nuclei (or seeds for the nuclei) from the finest resolution available. While the richest information has been utilized by such approaches, it is sometimes difficult to address the heterogeneity of nuclei in different tissues. In this work, we propose a hierarchical approach which starts from the lower resolution level and adaptively adjusts the parameters while progressing into finer and finer resolution. The algorithm is tested on brain and lung cancers images from The Cancer Genome Atlas data set.
KEYWORDS: Image segmentation, Neuroimaging, Brain, 3D image processing, Cerebral cortex, 3D image reconstruction, Image resolution, Data acquisition, Super resolution, Magnetic resonance imaging, Scanners, Analytical research
The hippocampus has been the focus of more imaging research than any other subcortical structure in the human brain. However a feature that has been almost universally overlooked are the bumpy ridges on the inferior aspect of the hippocampus, which we refer to as hippocampal dentation. These bumps arise from folds in the CA1 layer of Ammon's horn. Similar to the folding of the cerebral cortex, hippocampal dentation allows for greater surface area in a confined space. However, while quantitative studies of radiologic brain images have been advancing for decades, examining numerous approaches to hippocampal segmentation and morphology analysis, virtually all published 3D renderings of the hippocampus show the under surface to be quite smooth or mildly irregular; we have rarely seen the characteristic bumpy structure in the reconstructed 3D scene, one exception being the 9.4T postmortem study. This is presumably due to the fact that, based on our experience with high resolution images, there is a dramatic degree of variability in hippocampal dentation between individuals from very smooth to highly dentated. An apparent question is, does this indicate that this specific morphological signature can only be captured using expensive ultra-high field techniques? Or, is such information buried in the data we commonly acquire, awaiting a computation technique that can extract and render it clearly? In this study, we propose a super-resolution technique that captures the fine scale morphometric features of the hippocampus based on common T1-weighted 3T MR images.
In multi-atlas segmentation, one typically registers several atlases to the new image, and their respective segmented label images are transformed and fused to form the final segmentation. After each registration, the quality of the registration is reflected by the single global value: the final registration cost. Ideally, if the quality of the registration can be evaluated at each point, independent of the registration process, which also provides a direction in which the deformation can further be improved, the overall segmentation performance can be improved. We propose such a self-correcting multi-atlas segmentation method. The method is applied on hippocampus segmentation from brain images and statistically significantly improvement is observed.
Longitudinal analysis of medical imaging data has become central to the study of many disorders. Unfortunately, various constraints (study design, patient availability, technological limitations) restrict the acquisition of data to only a few time points, limiting the study of continuous disease/treatment progression. Having the ability to produce a sensible time interpolation of the data can lead to improved analysis, such as intuitive visualizations of anatomical changes, or the creation of more samples to improve statistical analysis. In this work, we model interpolation of medical image data, in particular shape data, using the theory of optimal mass transport (OMT), which can construct a continuous transition from two time points while preserving “mass” (e.g., image intensity, shape volume) during the transition. The theory even allows a short extrapolation in time and may help predict short-term treatment impact or disease progression on anatomical structure. We apply the proposed method to the hippocampus-amygdala complex in schizophrenia, the heart in atrial fibrillation, and full head MR images in traumatic brain injury.
Techniques in medical image analysis are many times used for the comparison or regression on the intensities of images. In general, the domain of the image is a given Cartesian grids. Shape analysis, on the other hand, studies the similarities and differences among spatial objects of arbitrary geometry and topology. Usually, there is no function defined on the domain of shapes. Recently, there has been a growing needs for defining and analyzing functions defined on the shape space, and a coupled analysis on both the shapes and the functions defined on them. Following this direction, in this work we present a coupled analysis for both images and shapes. As a result, the statistically significant discrepancies in both the image intensities as well as on the underlying shapes are detected. The method is applied on both brain images for the schizophrenia and heart images for atrial fibrillation patients.
Desorption electrospray ionization mass spectrometry (DESI-MS) provides a highly sensitive imaging technique for differentiating normal and cancerous tissue at the molecular level. This can be very useful, especially under intra-operative conditions where the surgeon has to make crucial decision about the tumor boundary. In such situations, the time it takes for imaging and data analysis becomes a critical factor. Therefore, in this work we utilize compressive sensing to perform the sparse sampling of the tissue, which halves the scanning time. Furthermore, sparse feature selection is performed, which not only reduces the dimension of data from about 104 to less than 50, and thus significantly shortens the analysis time. This procedure also identifies biochemically important molecules for further pathological analysis. The methods are validated on brain and breast tumor data sets.
The determination of myocardial volume at risk distal to coronary stenosis provides important information for prognosis
and treatment of coronary artery disease. In this paper, we present a novel computational framework for estimating the
myocardial volume at risk in computed tomography angiography (CTA) imagery. Initially, epicardial and endocardial
surfaces, and coronary arteries are extracted using an active contour method. Then, the extracted coronary arteries are
projected onto the epicardial surface, and each point on this surface is associated with its closest coronary artery using
the geodesic distance measurement. The likely myocardial region at risk on the epicardial surface caused by a stenosis is
approximated by the region in which all its inner points are associated with the sub-branches distal to the stenosis on the
coronary artery tree. Finally, the likely myocardial volume at risk is approximated by the volume in between the region
at risk on the epicardial surface and its projection on the endocardial surface, which is expected to yield computational
savings over risk volume estimation using the entire image volume. Furthermore, we expect increased accuracy since, as
compared to prior work using the Euclidean distance, we employ the geodesic distance in this work. The experimental
results demonstrate the effectiveness of the proposed approach on pig heart CTA datasets.
The Hamilton-Jacobi equation (HJE) appears widely in applied mathematics, physics, and optimal control
theory. While its analytical solution is rarely available, the numerical solver is indispensable. In this
work, firstly we propose a novel numerical method, based on the fast sweeping scheme, for the static HJE.
Comparing with the original fast sweeping method, our algorithm speeds up the solution up to 8 times in
3D. The efficiency is due to incorporating the ideas of the fast marching into the fast sweeping. Essentially,
the sweeping origin is selected so that the sweeping direction is more consistent with the information flow
direction and the regions where the two directions are against are avoided. Moreover, the successive-overrelaxation
nonlinear iterative method is used for faster convergence. Secondly, we provide a complete pipeline
for brain tractography, in which the proposed solver is the key component for finding the optimal fiber tracts.
Besides, the pipeline contains components from orientation distribution function estimation, multiple fiber
extraction to the final fiber bundle volumetric segmentation, completing the process from DW-MRI image to segmented fiber bundles. The pipeline is integrated into the publicly available software 3D Slicer. The new solver has been tested and compared with the original scheme on various types of HJEs and the tractography
pipeline was tested and performed consistently on all the 12 brain DW-MRI images.
Region based active contour model has been widely used in image segmentation on planar images. However
while a photo picture or a medical image is defined on 2D or 3D Euclidean spaces, in many cases the
information is defined on the curved surfaces, or more general manifolds. In this work we extend the region
based active contour method to work on the parametric manifolds. Essentially, it was noticed that in some
region based active contour segmentation methods, it were only the signs of the level set function values,
instead of the value themselves, which contribute to the cost functional. Thus a binary state function
is enough to represent the two phase segmentation. This gives an alternative view of the level set based
optimization and it is especially useful when the image domain is curved because the signed distance function
and its derivative are relative difficult to be evaluated in a curved space. Based on this, the segmentation
on the curved space is proceed by consecutively changing the binary state function, to optimize the cost functional. Finally, the converged binary function gives the segmentation on the manifold. The method is stable and fast. We demonstrate the applications of this method, with the cost functional defined using the Chan-Vese model, in neuroimaging, fluid mechanics and geographic fields where the information is naturally defined on curved surfaces.
Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers
of the heart, is a rapidly growing problem in modern societies. One treatment, referred to as catheter ablation,
targets specific parts of the left atrium for radio frequency ablation using an intracardiac catheter. Magnetic
resonance imaging has been used for both pre- and and post-ablation assessment of the atrial wall. Magnetic
resonance imaging can aid in selecting the right candidate for the ablation procedure and assessing post-ablation
scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which
facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the
computer-assisted segmentation of the left atrial wall, in this paper we use shape learning and shape-based image
segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.
An image, being a continuous function, is commonly discretely represented as a set of sample values, namely
the intensities, associated with the spatial grids. After that, all types of the operations are then carried
out there in. We denote such representation as the discrete function representation (DFR). In this paper
we provide another discrete representation for images using the point sets, called the point set representation
(PSR). Essentially, the image is normalized to have the unit integral and is treated as a probability
density function of some random variable. Then the PSR is formed by drawing samples of such random
variable. In contrast with the DFR, here the image is purely represented as points and no values are associated.
Besides being an equivalent discrete representation for images, we show that certain image operations
benefit from such representation in the numerical stability, performance, or both. Examples are given in
the Perona-Malik type diffusion where in the PSR there is no such problem as the numerical instability.
Furthermore, PSR naturally bridges the fields of image registration with the point set registration. This
helps handle some otherwise difficult problems in image registration such as partial image registration, with much faster convergence speed.
KEYWORDS: Image segmentation, Prostate, Magnetic resonance imaging, Magnetism, Medical imaging, Image registration, Statistical analysis, Biopsy, 3D image processing, Image processing algorithms and systems
In this paper, we present a shape based segmentation methodology for magnetic resonance prostate images.
We first propose a new way to represent shapes via the hyperbolic tangent of the signed distance function.
This effectively corrects the drawbacks of the signed distance function and yields very reasonable results for
the shape registration and learning. Secondly, under a Bayesian statistical framework, instead of computing
the posterior using a uniform prior, a directional distance map is introduced in order to incorporate a
priori knowledge of image content as well as the estimated center of target object. Essentially, the image
is modeled as a Finsler manifold and the metric is computed out of the directional derivative of the image.
Then the directional distance map is computed to suppress the posterior remote from the object center.
Thirdly, in the posterior image, a localized region based cost functional is designed to drive the shape
based segmentation. Such cost functional utilizes the local regional information and is robust to both image
noise and remote/irrelevant disturbances. With these three major components, the entire shape based segmentation procedure is provided as a complete open source pipeline and is applied to magnetic resonance image (MRI) prostate data.
Bayesian classification methods have been extensively used in a variety of image processing applications, including
medical image analysis. The basic procedure is to combine data-driven knowledge in the likelihood terms with
clinical knowledge in the prior terms to classify an image into a pre-determined number of classes. In many
applications, it is difficult to construct meaningful priors and, hence, homogeneous priors are assumed. In this
paper, we show how expectation-maximization weights and neighboring posterior probabilities may be combined
to make intuitive use of the Bayesian priors. Drawing upon insights from computer vision tracking algorithms,
we cast the problem in a tissue tracking framework. We show results of our algorithm on the classification of
gray and white matter along with surrounding cerebral spinal fluid in brain MRI scans. We show results of our
algorithm on 20 brain MRI datasets along with validation against expert manual segmentations.
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