nils-herrmann/clean_scirepeval huggingface dataset:
An automated method for finding curves of sulcal fundi on human cortical surfaces

We present a method for automatically finding curves representing the sulcal fundi on the human brain cortex. A flattened map of the cortical surface is used as the reference space in which the curves are modeled. The map is also used to transfer planar curves back to the cortical surface to extract sulcal fundal curves. Instead of modeling the curves by densely sampled landmark points, as it is done in the traditional active shape models, we model sulcal curves by a small number of anchor points that correspond to salient features, such as end points or points of intersections. The full sulcal curves connecting the anchor points are reconstructed by an extension of the fast marching method. Each anchor point carries a wavelet based attribute vector whose goal is to provide a distinctive morphological signature for the anchor point. This allows us to efficiently solve the problem in a low-dimensional space. Moreover, because each anchor point has this signature, and because anchor points are chosen to be salient features, the cost function defined in this low-dimensional space is presumed to have few local minima. Experimental results show that the sulcal curves extracted using the automatic method agrees well with the manually drawn sulcal curves.
Using a statistical shape model to extract sulcal curves on the outer cortex of the human brain

A method for automated segmentation of major cortical sulci on the outer brain boundary is presented, with emphasis on automatically determining point correspondence and on labeling cortical regions. The method is formulated in a general optimization framework defined on the unit sphere, which serves as parametric domain for convoluted surfaces of spherical topology. A statistical shape model, which includes a network of deformable curves on the unit sphere, seeks geometric features such as high curvature regions and labels such features via a deformation process that is confined within a spherical map of the outer brain boundary. The limitations of the customary spherical coordinate system, which include discontinuities at the poles and nonuniform sampling, are overcome by defining the statistical prior of shape variation in terms of projections of landmark points onto corresponding tangent planes of the sphere. The method is tested against and shown to be as accurate as manually defined segmentations.
Incompressible Deformation Estimation Algorithm (IDEA) From Tagged MR Images

Measuring the 3D motion of muscular tissues, e.g., the heart or the tongue, using magnetic resonance (MR) tagging is typically carried out by interpolating the 2D motion information measured on orthogonal stacks of images. The incompressibility of muscle tissue is an important constraint on the reconstructed motion field and can significantly help to counter the sparsity and incompleteness of the available motion information. Previous methods utilizing this fact produced incompressible motions with limited accuracy. In this paper, we present an incompressible deformation estimation algorithm (IDEA) that reconstructs a dense representation of the 3D displacement field from tagged MR images and the estimated motion field is incompressible to high precision. At each imaged time frame, the tagged images are first processed to determine components of the displacement vector at each pixel relative to the reference time. IDEA then applies a smoothing, divergence-free, vector spline to interpolate velocity fields at intermediate discrete times such that the collection of velocity fields integrate over time to match the observed displacement components. Through this process, IDEA yields a dense estimate of a 3D displacement field that matches our observations and also corresponds to an incompressible motion. The method was validated with both numerical simulation and in vivo human experiments on the heart and the tongue.
Adaptive fuzzy segmentation of magnetic resonance images

An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, the authors fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, they also describe a new faster multigrid-based algorithm for its implementation. They show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.
Automatic estimation of midsagittal plane and AC-PC alignment on nonrigid registration

Identifying the anterior and posterior commissures as well as the midsagittal plane in magnetic resonance brain images is a critical first step in the normalization process underlying group studies. We present a fully automatic algorithm that locates these structures with high accuracy. Estimates of all three landmarks are achieved first with a nonrigid registration algorithm and further optimization is used to refine these estimates. Extensive validation experiments involving both simulated and real data sets demonstrate the accuracy and robustness of the proposed method
Automatic sulcal curve extraction with MRF based shape prior

Extracting and labeling sulcal curves on the human cerebral cortex is important for many neuroscience studies, however manually annotating the sulcal curves is a time-consuming task. In this paper, we present an automatic sulcal curve extraction method by registering a set of dense landmark points representing the sulcal curves to the subject cortical surface. A Markov random field is used to model the prior distribution of these landmark points, with short edges in the graph preserving the curve structure and long edges modeling the global context of the curves. Our approach is validated using a leave-one-out strategy of training and evaluation on fifteen cortical surfaces, and a quantitative error analysis on the extracted major sulcal curves.
Robust unsupervised tissue classification in MR images

A general framework for performing robust, unsupervised tissue classification in magnetic resonance images is presented. Tissue classification is formulated as an estimation problem based on an imaging model. Prior models are used within the estimation problem to compensate for noise and intensity inhomogeneity artifacts. From this framework, approaches based on K-means clustering via the expectation-maximization algorithm, and fuzzy clustering can be derived. The performance of the different types of approaches are evaluated using both simulated and real neuroimaging data.
Brain image registration based on cortical contour mapping

The authors address the problem of brain image registration, and they present a new, nonlinear registration technique. In the first step of the authors' technique they obtain a point-to-point mapping between the outer cortical contours of the brain images using an elastic string algorithm. In the second step the authors register the two images based on the point-to-point correspondence established in the first step. They propose a new, nonlinear registration transformation, which is based on a spatially variable scaling and relation that can describe highly nonlinear deformations. Finally, the authors test their algorithm on two different registration problems: they first consider the registration of a postmortem photograph of a baboon brain cross-section and then an MR image of approximately the same cross-section.<<ETX>>
Patch based intensity normalization of brain MR images

Magnetic resonance (MR) imaging (MRI) is widely used to study the structure of human brains. Unlike computed tomography (CT), MR image intensities do not have a tissue specific interpretation. Thus images of the same subject obtained with either the same imaging sequence on different scanners or with differing parameters have widely varying intensity scales. This inconsistency introduces errors in segmentation, and other image processing tasks, thus necessitating image intensity standardization. Compared to previous intensity normalization methods using histogram transformations-which try to find a global one-to-one intensity mapping based on histograms-we propose a patch based generative model for intensity normalization between images acquired under different scanners or different pulse sequence parameters. Our method outperforms histogram based methods when normalizing phantoms simulated with various parameters. Additionally, experiments on real data, acquired under a variety of scanners and acquisition parameters, have more consistent segmentations after our normalization.
THREE-POINT METHOD FOR FAST AND ROBUST FIELD MAPPING FOR EPI GEOMETRIC DISTORTION CORRECTION

Because of its superb temporal resolution, echo planar imaging (EPI) is widely used for functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and monitoring dynamic processes. EPI suffers from geometric distortions caused by magnetic field inhomogeneities, and although these distortions can be corrected by field mapping, current methods require lengthy auxiliary image acquisitions and/or phase unwrapping. We present a 3-point method for field mapping that circumvents these problems and is robust to additive noise. Results demonstrate that the performance of this approach is markedly better than the standard two-point method and is comparable to methods requiring much longer auxiliary image acquisitions
3D displacement field reconstruction from planar tagged cardiac MR images

A major problem in cardiac imaging is the measurement of cardiac motion for identification of ischemic and infarcted tissues. W.G. O'Dell et al. ( Imag., vol.3(p), p.208, 1993) have recently proposed a method that uses magnetic resonance tag patterns to measure the 3D displacement field of the myocardium. The measurements are sparse, however, and interpolation is required to reconstruct a dense displacement field. Here, the authors propose a method for computing a dense displacement field on a regular 3D lattice from sparse displacement measurements. This method uses a multidimensional stochastic model for the true displacement field and the Fisher estimation framework to estimate a displacement vector at each point the lattice. Simulation results are presented that demonstrate the accuracy of the authors' technique.<<ETX>>
MRI analysis of 3D normal and post-glossectomy tongue motion in speech

Measuring the internal muscular motion and deformation of the tongue during natural human speech is of high interest to head and neck surgeons and speech language pathologists. A pipeline for calculating 3D tongue motion from dynamic cine and tagged Magnetic Resonance (MR) images during speech has been developed. This paper presents the result of a complete analysis of eleven subjects' (seven normal controls and four glossectomy patients) global tongue motion during speech obtained through MR imaging and processed through the tongue motion analysis pipeline. The data is regularized into the same framework for comparison. A generalized two-step principal component analysis is used to show the major difference between patients' and controls' tongue motions. A test is performed to demonstrate the ability of this process to distinguish patient data from control data and to show the potential power of quantitative analysis that the tongue motion pipeline can achieve.
Magnetic resonance image synthesis through patch regression

Magnetic resonance imaging (MRI) is widely used for analyzing human brain structure and function. MRI is extremely versatile and can produce different tissue contrasts as required by the study design. For reasons such as patient comfort, cost, and improving technology, certain tissue contrasts for a cohort analysis may not have been acquired during the imaging session. This missing pulse sequence hampers consistent neuroanatomy research. One possible solution is to synthesize the missing sequence. This paper proposes a data-driven approach to image synthesis, which provides equal, if not superior synthesis compared to the state-of-the-art, in addition to being an order of magnitude faster. The synthesis transformation is done on image patches by a trained bagged ensemble of regression trees. Validation was done by synthesizing T2-weighted contrasts from T1-weighted scans, for phantoms and real data. We also synthesized 3 Tesla T1-weighted magnetization prepared rapid gradient echo (MPRAGE) images from 1.5 Tesla MPRAGEs to demonstrate the generality of this approach.
Optical flow for tagged MR images

A method to reconstruct motion from sequences of tagged magnetic resonance (MR) images is presented. MR tagging is used to create a spatial pattern of varying magnetization so that objects which may otherwise have constant intensity are textured; this reduces the motion ambiguity associated with the aperture problem in computer vision. To account for the decay of the tag pattern, a new optical flow algorithm is developed and implemented. Velocity fields estimated using this algorithm are used to recursively update the implied motion reference map over time, thereby tracking the motion of individual particles. If a segmentation of the object is known at the time the tag pattern is created, then an object may be selectively tracked, using the estimated reference map to update the object's position as time progresses. Results are shown for actual MR phantom data.<<ETX>>
Surgical fiducial segmentation and tracking for pose estimation based on ultrasound B-mode images.

Doppler ultrasound is a non-invasive diagnostic tool for the quantitative measurement of blood flow. However, given that it provides velocity data that is dependent on the location and angle of measurement, repeat measurements to detect problems over time may require an expert to return to the same location. We therefore developed an image-guidance system based on ultrasound B-mode images that enables an inexperienced user to position the ultrasound probe at the same site repeatedly in order to acquire a comparable time series of Doppler readings. The system utilizes a bioresorbable fiducial and complementing software composed of the fiducial detection, key points tracking, probe pose estimation, and graphical user interface (GUI) modules. The fiducial is an echogenic marker that is implanted at the surgical site and can be detected and tracked during ultrasound B-mode screening. The key points on the marker can next be used to determine the pose of the ultrasound probe with respect to the marker. The 3D representation of the probe with its position and orientation are then displayed in the GUI for the user guidance. The fiducial detection has been tested on the data sets collected from three animal studies. The pose estimation algorithm was validated by five data sets collected by a UR5 robot. We tested the system on a plastisol phantom and showed that it can detect and track the fiducial marker while displaying the probe pose in real-time.
Multimodal Registration via Mutual Information Incorporating Geometric and Spatial Context

Multimodal image registration is a class of algorithms to find correspondence from different modalities. Since different modalities do not exhibit the same characteristics, finding accurate correspondence still remains a challenge. To deal with this, mutual information (MI)-based registration has been a preferred choice as MI is based on the statistical relationship between both volumes to be registered. However, MI has some limitations. First, MI-based registration often fails when there are local intensity variations in the volumes. Second, MI only considers the statistical intensity relationships between both volumes and ignores the spatial and geometric information about the voxel. In this work, we propose to address these limitations by incorporating spatial and geometric information via a 3D Harris operator. In particular, we focus on the registration between a high-resolution image and a low-resolution image. The MI cost function is computed in the regions where there are large spatial variations such as corner or edge. In addition, the MI cost function is augmented with geometric information derived from the 3D Harris operator applied to the high-resolution image. The robustness and accuracy of the proposed method were demonstrated using experiments on synthetic and clinical data including the brain and the tongue. The proposed method provided accurate registration and yielded better performance over standard registration methods.
A Method for the Reduction of Blurring Due to Registration Error in Compound Ultrasound Images

The most intractable source of b lurring in cornpound B-mode images is the unknown highly variable sonic velocity along the many ray paths. Our objective was to investigate new image acquisition and processing methods to reduce the blurring effect of these errors. A scanner was constructed u sing a 5 MHz transducer with a 16 mm diameter and 70 mm focal d istance. Rectilinear scans are produced by translation, while the angle of incidence of a scan is changed by rotation of the transducer. The range of rotation was +/- 15 degrees and the angular increment was 1.0 degree. To simulate the refraction problem a variable thickness layer of fluid was inserted between the transducer and the object. Compound images produced with this apparatus are blurred due to refraction at the fluid layer interfaces. A method is reported here for producing a compound image with less blur by realigning the series of simple images, prior to compounding, using a cross c orrelation algorithm on selected t argets. Good results have been demonstrated on artificial phantoms. Further studies using excised human organs are under way, as is a more comprehensive analysis of the image processing algorithm.
Prostate Brachytherapy Seed Reconstruction With Gaussian Blurring and Optimal Coverage Cost

Intraoperative dosimetry in prostate brachytherapy requires localization of the implanted radioactive seeds. A tomosynthesis-based seed reconstruction method is proposed. A three-dimensional volume is reconstructed from Gaussian-blurred projection images and candidate seed locations are computed from the reconstructed volume. A false positive seed removal process, formulated as an optimal coverage problem, iteratively removes ldquoghostrdquo seeds that are created by tomosynthesis reconstruction. In an effort to minimize pose errors that are common in conventional C-arms, initial pose parameter estimates are iteratively corrected by using the detected candidate seeds as fiducials, which automatically ldquofocusesrdquo the collected images and improves successive reconstructed volumes. Simulation results imply that the implanted seed locations can be estimated with a detection rate of ges97.9% and ges99.3% from three and four images, respectively, when the C-arm is calibrated and the pose of the C-arm is known. The algorithm was also validated on phantom data sets successfully localizing the implanted seeds from four or five images. In a Phase-1 clinical trial, we were able to localize the implanted seeds from five intraoperative fluoroscopy images with 98.8% (STD=1.6) overall detection rate.
Optimal brightness patterns for 2-D optical flow

The authors describe a method for finding the optimal brightness function by formulating optical flow as an optimal linear smoothing problem and deriving an a priori performance measure based on the estimation error covariance and the effect of the temporal derivative approximation error. It is shown that the performance of optical flow is mainly a function of the curvature of the brightness function. At low curvatures, there is little variation in the spatial gradient across the image and the underlying motion is obscured by the aperture problem. At high curvatures, the numerical computation of the temporal derivative increases the measurement noise variance, and the resulting optimal estimate is oversmoothed. The optimal brightness function represents the optimal tradeoff between these two effects.<<ETX>>
Regenerating MR tagged images using harmonic phase (HARP) methods

Magnetic resonance tagging has proven useful in the visualization and quantification of cardiac motion. Traditionally, tags are designed to have crisp geometric profiles in order to enhance both visualization and detection of tags. Recent image acquisition and analysis methods, however, have been designed to exploit sinusoidal tag profiles. This paper presents a method based on harmonic phase (HARP) concepts to synthesize tag lines that have both crisp profiles and alternative orientations from the original sinusoidal patterns. Results are demonstrated on images acquired with SPAMM, CSPAMM, and fast-HARP pulse sequences.
Fast numerical scheme for gradient vector flow computation using a multigrid method

The gradient vector flow (GVF) deformable model was introduced by Xu and Prince as an effective approach to overcome the limited capture range problem of classical deformable models and their inability to progress into boundary concavities. It has found many important applications in the area of medical image processing. The simple iterative method proposed in the original work on GVF, however, is slow to converge. A new multigrid method is proposed for GVF computation on 2D and 3D images. Experimental results show that the new implementation significantly improves the computational speed by at least an order of magnitude, which facilitates the application of GVF deformable models in processing large medical images.
MR contrast synthesis for lesion segmentation

The magnetic resonance contrast of a neuroimaging data set has strong impact on the utility of the data in image analysis tasks, such as registration and segmentation. Lengthy acquisition times often prevent routine acquisition of multiple MR contrast images, and opportunities for detailed analysis using these data would seem to be irrevocably lost. This paper describes an example based approach which uses patch matching from a multiple contrast atlas with the intended goal of generating an alternate MR contrast image, thus effectively simulating alternative pulse sequences from one another. In this paper, we deal specifically with Fluid Attenuated Inversion Recovery (FLAIR) sequence generation from T1 and T2 pulse sequences. The applicability of this synthetic FLAIR for estimating white matter lesions segmentation is demonstrated.
Optimal brightness functions for optical flow estimation of deformable motion

Estimation accuracy of Horn and Schunck's (1981) classical optical flow algorithm depends on many factors including the brightness pattern of the measured images. Since some applications can select brightness functions with which to "paint" the object, it is desirable to know what patterns will lead to the best motion estimates. The paper presents a method for determining this pattern a priori using mild assumptions about the velocity field and imaging process. The method is based on formulating Horn and Schunck's algorithm as a linear smoother and rigorously deriving an expression for the corresponding error covariance function. The authors then specify a scalar performance measure and develop an approach to select an optimal brightness function which minimizes this performance measure from within a parametrized class. Conditions for existence of an optimal brightness function are also given. The resulting optimal performance is demonstrated using simulations, and a discussion of these results and potential future research is given.