Independent real-world application of a clinical-grade automated prostate cancer detection system.

Artificial intelligence (AI)-based systems applied to histopathology whole-slide images have the potential to improve patient care through mitigation of challenges posed by diagnostic variability, histopathology caseload, and shortage of pathologists. We sought to define the performance of an AI-based automated prostate cancer detection system, Paige Prostate, when applied to independent real-world data. The algorithm was employed to classify slides into two categories: benign (no further review needed) or suspicious (additional histologic and/or immunohistochemical analysis required).

Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies.

Prostate cancer (PrCa) is the second most common cancer among men in the United States. The gold standard for detecting PrCa is the examination of prostate needle core biopsies. Diagnosis can be challenging, especially for small, well-differentiated cancers.

Precise measurement of coronary stenosis diameter with CCTA using CT number calibration.

Purpose: Coronary x-ray computed tomography angiography (CCTA) continues to develop as a noninvasive method for the assessment of coronary vessel geometry and the identification of physiologically significant lesions. The uncertainty of quantitative lesion diameter measurement due to limited spatial resolution and vessel motion reduces the accuracy of CCTA diagnoses. In this paper, we introduce a new technique called computed tomography (CT)-number-Calibrated Diameter to improve the accuracy of the vessel and stenosis diameter measurements with CCTA.

Generation of Patient-Specific Cardiac Vascular Networks: A Hybrid Image-Based and Synthetic Geometric Model.

Objective: In this paper, we propose an algorithm for the generation of a patient-specific cardiac vascular network starting from segmented epicardial vessels down to the arterioles.

Method: We extend a tree generation method based on satisfaction of functional principles, named constrained constructive optimization, to account for multiple, competing vascular trees. The algorithm simulates angiogenesis under vascular volume minimization with flow-related and geometrical constraints adapting the simultaneous tree growths to patient priors.

Lumen boundaries extracted from coronary computed tomography angiography on computed fractional flow reserve (FFRCT): validation with optical coherence tomography.

Aims: The aim of this study was to evaluate the accuracy of minimum lumen area (MLA) by coronary computed tomography angiography (cCTA) and its impact on fractional flow reserve (FFRCT).

Methods And Results: Fifty-seven patients (118 lesions, 72 vessels) who underwent cCTA and optical coherence tomography (OCT) were enrolled. OCT and cCTA were co-registered and MLAs were measured with both modalities.

Automated estimation of image quality for coronary computed tomographic angiography using machine learning.

Objectives: Our goal was to evaluate the efficacy of a fully automated method for assessing the image quality (IQ) of coronary computed tomography angiography (CCTA).

Methods: The machine learning method was trained using 75 CCTA studies by mapping features (noise, contrast, misregistration scores, and un-interpretability index) to an IQ score based on manual ground truth data. The automated method was validated on a set of 50 CCTA studies and subsequently tested on a new set of 172 CCTA studies against visual IQ scores on a 5-point Likert scale.

Identification of High-Risk Plaques Destined to Cause Acute Coronary Syndrome Using Coronary Computed Tomographic Angiography and Computational Fluid Dynamics.

Objectives: The authors investigated the utility of noninvasive hemodynamic assessment in the identification of high-risk plaques that caused subsequent acute coronary syndrome (ACS).

Background: ACS is a critical event that impacts the prognosis of patients with coronary artery disease. However, the role of hemodynamic factors in the development of ACS is not well-known.

Contrast Driven Elastica for Image Segmentation.

Minimization of boundary curvature is a classic regularization technique for image segmentation in the presence of noisy image data. Techniques for minimizing curvature have historically been derived from gradient descent methods which could be trapped by a local minimum and, therefore, required a good initialization. Recently, combinatorial optimization techniques have overcome this barrier by providing solutions that can achieve a global optimum.

Fast Computation of Hemodynamic Sensitivity to Lumen Segmentation Uncertainty.

Patient-specific blood flow modeling combining imaging data and computational fluid dynamics can aid in the assessment of coronary artery disease. Accurate coronary segmentation and realistic physiologic modeling of boundary conditions are important steps to ensure a high diagnostic performance. Segmentation of the coronary arteries can be constructed by a combination of automated algorithms with human review and editing.

Real-time sensitivity analysis of blood flow simulations to lumen segmentation uncertainty.

Patient-specific modeling of blood flow combining CT image data and computational fluid dynamics has significant potential for assessing the functional significance of coronary artery disease. An accurate segmentation of the coronary arteries, an essential ingredient for blood flow modeling methods, is currently attained by a combination of automated algorithms with human review and editing. However, not all portions of the coronary artery tree affect blood flow and pressure equally, and it is of significant importance to direct human review and editing towards regions that will most affect the subsequent simulations.

Learning the manifold of quality ultrasound acquisition.

Ultrasound acquisition is a challenging task that requires simultaneous adjustment of several acquisition parameters (the depth, the focus, the frequency and its operation mode). If the acquisition parameters are not properly chosen, the resulting image will have a poor quality and will degrade the patient diagnosis and treatment workflow. Several hardware-based systems for autotuning the acquisition parameters have been previously proposed, but these solutions were largely abandoned because they failed to properly account for tissue inhomogeneity and other patient-specific characteristics.

FOCUSR: feature oriented correspondence using spectral regularization--a method for precise surface matching.

Existing methods for surface matching are limited by the tradeoff between precision and computational efficiency. Here, we present an improved algorithm for dense vertex-to-vertex correspondence that uses direct matching of features defined on a surface and improves it by using spectral correspondence as a regularization. This algorithm has the speed of both feature matching and spectral matching while exhibiting greatly improved precision (distance errors of 1.

Sparsity-promoting calibration for GRAPPA accelerated parallel MRI reconstruction.

The amount of calibration data needed to produce images of adequate quality can prevent auto-calibrating parallel imaging reconstruction methods like generalized autocalibrating partially parallel acquisitions (GRAPPA) from achieving a high total acceleration factor. To improve the quality of calibration when the number of auto-calibration signal (ACS) lines is restricted, we propose a sparsity-promoting regularized calibration method that finds a GRAPPA kernel consistent with the ACS fit equations that yields jointly sparse reconstructed coil channel images. Several experiments evaluate the performance of the proposed method relative to unregularized and existing regularized calibration methods for both low-quality and underdetermined fits from the ACS lines.

Evaluating segmentation error without ground truth.

The automatic delineation of the boundaries of organs and other anatomical structures is a key component of many medical image processing systems. In this paper we present a generic learning approach based on a novel space of segmentation features, which can be trained to predict the overlap error and Dice coefficient of an arbitrary organ segmentation without knowing the ground truth delineation. We show the regressor to be much stronger a predictor of these error metrics than the responses of probabilistic boosting classifiers trained on the segmentation boundary.

Network, anatomical, and non-imaging measures for the prediction of ADHD diagnosis in individual subjects.

Brain imaging methods have long held promise as diagnostic aids for neuropsychiatric conditions with complex behavioral phenotypes such as Attention-Deficit/Hyperactivity Disorder. This promise has largely been unrealized, at least partly due to the heterogeneity of clinical populations and the small sample size of many studies. A large, multi-center dataset provided by the ADHD-200 Consortium affords new opportunities to test methods for individual diagnosis based on MRI-observable structural brain attributes and functional interactions observable from resting-state fMRI.

Denoising sparse images from GRAPPA using the nullspace method.

To accelerate magnetic resonance imaging using uniformly undersampled (nonrandom) parallel imaging beyond what is achievable with generalized autocalibrating partially parallel acquisitions (GRAPPA) alone, the DEnoising of Sparse Images from GRAPPA using the Nullspace method is developed. The trade-off between denoising and smoothing the GRAPPA solution is studied for different levels of acceleration. Several brain images reconstructed from uniformly undersampled k-space data using DEnoising of Sparse Images from GRAPPA using the Nullspace method are compared against reconstructions using existing methods in terms of difference images (a qualitative measure), peak-signal-to-noise ratio, and noise amplification (g-factors) as measured using the pseudo-multiple replica method.

Random walks based multi-image segmentation: Quasiconvexity results and GPU-based solutions.

We recast the problem using Random Walker (RW) segmentation as the core segmentation algorithm, rather than the traditional MRF approach adopted in the literature so far. Our formulation is similar to previous approaches in the sense that it also permits Cosegmentation constraints (which impose consistency between the extracted objects from ≥ 2 images) using a nonparametric model. However, several previous nonparametric cosegmentation methods have the serious limitation that they require adding one auxiliary node (or variable) for every pair of pixels that are similar (which effectively limits such methods to describing only those objects that have high entropy appearance models).

Regurgitation quantification using 3D PISA in volume echocardiography.

We present the first system for measurement of proximal isovelocity surface area (PISA) on a 3D ultrasound acquisition using modified ultrasound hardware, volumetric image segmentation and a simple efficient workflow. Accurate measurement of the PISA in 3D flow through a valve is an emerging method for quantitatively assessing cardiac valve regurgitation and function. Current state of the art protocols for assessing regurgitant flow require laborious and time consuming user interaction with the data, where a precise execution is crucial for an accurate diagnosis.

Fast brain matching with spectral correspondence.

Brain matching is an important problem in neuroimaging studies. Current surface-based methods for cortex matching and atlasing, although quite accurate, can require long computation times. Here we propose an approach based on spectral correspondence, where spectra of graphs derived from the surface model meshes are matched.

Global error minimization in image mosaicing using graph connectivity and its applications in microscopy.

Several applications such as multiprojector displays and microscopy require the mosaicing of images (tiles) acquired by a camera as it traverses an unknown trajectory in 3D space. A homography relates the image coordinates of a point in each tile to those of a reference tile provided the 3D scene is planar. Our approach in such applications is to first perform pairwise alignment of the tiles that have imaged common regions in order to recover a homography relating the tile pair.

Power Watershed: A Unifying Graph-Based Optimization Framework.

In this work, we extend a common framework for graph-based image segmentation that includes the graph cuts, random walker, and shortest path optimization algorithms. Viewing an image as a weighted graph, these algorithms can be expressed by means of a common energy function with differing choices of a parameter q acting as an exponent on the differences between neighboring nodes. Introducing a new parameter p that fixes a power for the edge weights allows us to also include the optimal spanning forest algorithm for watershed in this same framework.

Minimal surfaces extend shortest path segmentation methods to 3D.

Shortest paths have been used to segment object boundaries with both continuous and discrete image models. Although these techniques are well defined in 2D, the character of the path as an object boundary is not preserved in 3D. An object boundary in three dimensions is a 2D surface.

The piecewise smooth Mumford-Shah functional on an arbitrary graph.

The Mumford-Shah functional has had a major impact on a variety of image analysis problems, including image segmentation and filtering, and, despite being introduced over two decades ago, it is still in widespread use. Present day optimization of the Mumford-Shah functional is predominated by active contour methods. Until recently, these formulations necessitated optimization of the contour by evolving via gradient descent, which is known for its overdependence on initialization and the tendency to produce undesirable local minima.

Combining registration and minimum surfaces for the segmentation of the left ventricle in cardiac cine MR images.

This paper describes a system to automatically segment the left ventricle in all slices and all phases of cardiac cine magnetic resonance datasets. After localizing the left ventricle blood pool using motion, thresholding and clustering, slices are segmented sequentially. For each slice, deformable registration is used to align all the phases, candidates contours are recovered in the average image using shortest paths, and a minimal surface is built to generate the final contours.

Weights and topology: a study of the effects of graph construction on 3D image segmentation.

Graph-based algorithms have become increasingly popular for medical image segmentation. The fundamental process for each of these algorithms is to use the image content to generate a set of weights for the graph and then set conditions for an optimal partition of the graph with respect to these weights. To date, the heuristics used for generating the weighted graphs from image intensities have largely been ignored, while the primary focus of attention has been on the details of providing the partitioning conditions.