nils-herrmann/clean_scirepeval huggingface dataset:
A Cross Validation Study of Deep Brain Stimulation Targeting: From Experts to Atlas-Based, Segmentation-Based and Automatic Registration Algorithms

Validation of image registration algorithms is a difficult task and open-ended problem, usually application-dependent. In this paper, we focus on deep brain stimulation (DBS) targeting for the treatment of movement disorders like Parkinson's disease and essential tremor. DBS involves implantation of an electrode deep inside the brain to electrically stimulate specific areas shutting down the disease's symptoms. The subthalamic nucleus (STN) has turned out to be the optimal target for this kind of surgery. Unfortunately, the STN is in general not clearly distinguishable in common medical imaging modalities. Usual techniques to infer its location are the use of anatomical atlases and visible surrounding landmarks. Surgeons have to adjust the electrode intraoperatively using electrophysiological recordings and macrostimulation tests. We constructed a ground truth derived from specific patients whose STNs are clearly visible on magnetic resonance (MR) T2-weighted images. A patient is chosen as atlas both for the right and left sides. Then, by registering each patient with the atlas using different methods, several estimations of the STN location are obtained. Two studies are driven using our proposed validation scheme. First, a comparison between different atlas-based and nonrigid registration algorithms with a evaluation of their performance and usability to locate the STN automatically. Second, a study of which visible surrounding structures influence the STN location. The two studies are cross validated between them and against expert's variability. Using this scheme, we evaluated the expert's ability against the estimation error provided by the tested algorithms and we demonstrated that automatic STN targeting is possible and as accurate as the expert-driven techniques currently used. We also show which structures have to be taken into account to accurately estimate the STN location
Real-time treatment planning optimisation for brachytherapy

In this paper, we present an integrated system for real-time dose distribution calculation and treatment planning optimisation for brachytherapy of prostate cancer, with a special emphasis on the visual integration of the dosimetry and target images obtained from the open magnetic resonance system. This system involves a fast method to calculate dose distributions of multiple concurrent radioactive sources, based on the combination of elements from a database of pre-calculated dose distribution maps for single sources, combined linearly to provide the final dose distribution map. Simulated annealing, in conjunction with the inverse planning method, is used to determine the source dwell times at preselected locations in order to optimally irradiate the tumour while preserving the surrounding healthy tissues. This algorithm, implemented in FORTRAN, is integrated into a computer-assisted treatment planning tool, written in JAVA, using the runtime class and RMI API of Java. The whole system is now under clinical testing at the Geneva University Hospital.
Towards robust cascaded regression for face alignment in the wild

Most state-of-the-art solutions for localizing facial feature landmarks build on the recent success of the cascaded regression framework [7, 15, 34], which progressively predicts the shape update based on the previous shape estimate and its feature calculation. We revisit several core aspects of this framework and show that proper selection of regression method, local image feature and fine-tuning of further fitting strategies can achieve top performance for face alignment using the generic cascaded regression algorithm. In particular, our strongest model features Iteratively Reweighted Least Squares (IRLS) [18] for training robust regressors in the presence of outliers in the training data, RootSIFT  as the image patch descriptor that replaces the original Euclidean distance in SIFT [24] with the Hellinger distance, as well as coarse-to-fine fitting and in-plane pose normalization during shape update. We show the benefit of each proposed improvement by extensive individual experiments compared to the baseline approach  on the LFPW dataset [4]. On the currently most challenging 300-W dataset [28] and COFW dataset [4], we report state-of-the-art results that are superior to or on par with recently published algorithms.
Learning the weight matrix for sparsity averaging in compressive imaging

We propose to map the fast iterative soft thresholding algorithm to a deep neural network (DNN), with a sparsity prior in a concatenation of wavelet bases, in the context of compressive imaging. We exploit the DNN architecture to learn the optimal weight matrix of the corresponding reweighted ℓ1-minimization problem. We later use the learned weight matrix for the image reconstruction process, which is recast as a simple ℓ1-minimization problem. The approach, denoted as learned extended FISTA, shows promising results in terms of image quality, compared to state-of-the art algorithms, and significantly reduces the reconstruction time required to solve the reweighted ℓ1-minimization problem.
Apodization scheme for hardware-efficient beamformer

3D ultrasound is an emerging diagnostic technique that extends standard ultrasound imaging by capturing volumes, instead of planes. This brings completely new diagnostic opportunities, among which the possibility of disjoining image acquisition and analysis, thus enabling remote diagnosis, which would bring obvious medical and economic benefits. Unfortunately, 3D ultrasound is several orders of magnitude more computationally complex than 2D imaging. Therefore, algorithmic improvements to simplify the processing are mandatory in order to conceive cheap, portable, low-power imagers. The kernel of the 3D imaging process, called beamforming, consists essentially of computing delay and apodization profiles. We have previously devised an approximation of the delay calculation stage, which dramatically reduces hardware complexity. Unfortunately, this approximation introduces an intrinsic degree of inaccuracy that can be characterized as added image noise. In this paper, we identify an efficient approximated approach to the calculation of apodization profiles, that additionally minimizes (-76%) the error introduced during delay calculation. Together, these two techniques enable an efficient computation of 3D ultrasound images.
Non-invasive monitoring of pulmonary artery pressure at the bedside.

Current solutions for the monitoring of pulmonary artery pressure (PAP) in patients suffering from pulmonary hypertension are limited to invasive means. Non-invasive alternatives, such as Doppler echocardiography, are incompatible with continuous monitoring due to their dependency on qualified personnel to perform the measurements. In the present study, a novel non-invasive and unsupervised approach based on the use of electrical impedance tomography (EIT) is presented. The approach was evaluated in three healthy subjects undergoing hypoxia-induced variations in PAP. A timing parameter - physiologically linked to the PAP via the so-called pulse wave velocity principle - was automatically extracted from the EIT data. Reference systolic PAP estimates were obtained by echocardiography. Strong correlation scores (r e [0.844, 0.990]) were found between the EIT-derived parameter and the reference PAP, thereby suggesting the validity of the proposed approach. If confirmed in larger datasets, these findings could open the way for a new branch of fully non-invasive hemodynamic monitors for patients with pulmonary hypertension.
Relative anatomical location for statistical non-parametric brain tissue classification in MR images

We propose a statistical nonparametric classification of brain tissues from an MR image based on the voxel intensities and on the relative anatomical location of the different tissues. We generate an artificial image component as the distance from the edges of the segmented brain. The nonparametric k-nearest neighbors rule (k-NN) is used since it requires no a priori information on the probability distribution of this distance component. The k-NN rule is also tested using different metrics (Euclidean, weighted Euclidean, Mahalanobis) in the classification space to define what "nearest neighbors" are. The results are twofold: firstly we show that all metrics perform well in ideal conditions, but that the Mahalanobis (and to some extent the weighted Euclidean) metric is more robust in the case of under-training of the classifier. Secondly we show that using the relative anatomical location in combination with the intensity information improves the classification of the tissues.
Information Theoretic Feature Extraction for Audio-Visual Speech Recognition

The problem of feature selection has been thoroughly analyzed in the context of pattern classification, with the purpose of avoiding the curse of dimensionality. However, in the context of multimodal signal processing, this problem has been studied less. Our approach to feature extraction is based on information theory, with an application on multimodal classification, in particular audio-visual speech recognition. Contrary to previous work in information theoretic feature selection applied to multimodal signals, our proposed methods penalize features for their redundancy, achieving more compact feature sets and better performance. We propose two greedy selection algorithms, one that penalizes a proportion of feature redundancy, while the other uses conditional mutual information as an evaluation measure, for the selection of visual features for audio-visual speech recognition. Our features perform better than linear discriminant analysis, the most usual transform for dimensionality reduction in the field, across a wide range of dimensionality values and combined with audio at different quality levels.
Weighted Shape-Based Averaging With Neighborhood Prior Model for Multiple Atlas Fusion-Based Medical Image Segmentation

In medical imaging, merging automated segmentations obtained from multiple atlases has become a standard practice for improving the accuracy. In this letter, we propose two new fusion methods: “Global Weighted Shape-Based Averaging” (GWSBA) and “Local Weighted Shape-Based Averaging” (LWSBA). These methods extend the well known Shape-Based Averaging (SBA) by additionally incorporating the similarity information between the reference (i.e., atlas) images and the target image to be segmented. We also propose a new spatially-varying similarity-weighted neighborhood prior model, and an edge-preserving smoothness term that can be used with many of the existing fusion methods. We first present our new Markov Random Field (MRF) based fusion framework that models the above mentioned information. The proposed methods are evaluated in the context of segmentation of lymph nodes in the head and neck 3D CT images, and they resulted in more accurate segmentations compared to the existing SBA.
COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography

Tractography is a class of algorithms aiming at in vivo mapping the major neuronal pathways in the white matter from diffusion magnetic resonance imaging (MRI) data. These techniques offer a powerful tool to noninvasively investigate at the macroscopic scale the architecture of the neuronal connections of the brain. However, unfortunately, the reconstructions recovered with existing tractography algorithms are not really quantitative even though diffusion MRI is a quantitative modality by nature. As a matter of fact, several techniques have been proposed in recent years to estimate, at the voxel level, intrinsic microstructural features of the tissue, such as axonal density and diameter, by using multicompartment models. In this paper, we present a novel framework to reestablish the link between tractography and tissue microstructure. Starting from an input set of candidate fiber-tracts, which are estimated from the data using standard fiber-tracking techniques, we model the diffusion MRI signal in each voxel of the image as a linear combination of the restricted and hindered contributions generated in every location of the brain by these candidate tracts. Then, we seek for the global weight of each of them, i.e., the effective contribution or volume, such that they globally fit the measured signal at best. We demonstrate that these weights can be easily recovered by solving a global convex optimization problem and using efficient algorithms. The effectiveness of our approach has been evaluated both on a realistic phantom with known ground-truth and in vivo brain data. Results clearly demonstrate the benefits of the proposed formulation, opening new perspectives for a more quantitative and biologically plausible assessment of the structural connectivity of the brain.
Soft Tissue Artifact Assessment During Treadmill Walking in Subjects With Total Knee Arthroplasty

Accurate measurement of knee kinematics during functional activities suffers mainly from soft tissue artifact (STA): the combination of local surface deformations and rigid movement of markers relative to the underlying bone (also called rigid STA movement: RSTAM). This study proposes to assess RSTAM on the thigh, shank, and knee joint and to observe possible features between subjects. Nineteen subjects with knee arthroplasty were asked to walk on a treadmill while a biplane fluoroscopic system (X-rays) and a stereophotogrammetric system (skin markers) recorded their knee movement. The RSTAM was defined as the rigid movement of the cluster of skin markers relative to the prosthesis. The results showed that RSTAM amplitude represents approximately 80-100% of the STA. The vertical axis of the anatomical frame of the femur was influenced the most by RSTAM. Combined with tibial error, internal/external rotation angle and distraction-compression were the knee kinematics parameters most affected by RSTAM during the gait cycle, with average rms values of 3.8° and 11.1 mm. This study highlighted higher RSTAM during the swing phase particularly in the thigh segment and suggests new features for RSTAM such as the particular shape of some RSTAM waveforms and the absence of RSTAM in certain kinematics during the gait phases. The comparison of coefficient of multiple correlations showed some similarities of RSTAM between subjects, while some correlations were found with gait speed and BMI. These new insights could potentially allow the development of new methods of compensation to avoid STA.
A deep learning approach to ultrasound image recovery

Based on the success of deep neural networks for image recovery, we propose a new paradigm for the compression and decompression of US signals which relies on a stacked denoising autoencoders. The first layer of the network is used to compress the signals and the remaining layers perform the reconstruction. We train the network on simulated US signals and evaluate its quality on images of the publicly available PICMUS dataset. We demonstrate that such a simple architecture outperforms state-of-the art methods, based on the compressed sensing framework, both in terms of image quality and computational complexity.
Sparsity Averaging for Compressive Imaging

We discuss a novel sparsity prior for compressive imaging in the context of the theory of compressed sensing with coherent redundant dictionaries, based on the observation that natural images exhibit strong average sparsity over multiple coherent frames. We test our prior and the associated algorithm, based on an analysis reweighted formulation, through extensive numerical simulations on natural images for spread spectrum and random Gaussian acquisition schemes. Our results show that average sparsity outperforms state-of-the-art priors that promote sparsity in a single orthonormal basis or redundant frame, or that promote gradient sparsity. Code and test data are available at https://github.com/basp-group/sopt.
On performance evaluation of face detection and localization algorithms

When comparing different methods for face detection or localization, one realizes that just simply comparing the reported results is misleading as, even if the results are reported on the same dataset, different authors have different views of what a correct detection/localization means. This paper addresses exactly this problem, proposing an objective measure for the goodness of a detection/localization for the case of frontal faces. The usage of the proposed technique insures a fair and unbiased way of reporting the results, making the experiment repeatable, measurable, and comparable by anybody else.
Facial Image Analysis for Fully Automatic Prediction of Difficult Endotracheal Intubation

Difficult tracheal intubation is a major cause of anesthesia-related injuries with potential life threatening complications. Detection and anticipation of difficult airway in the preoperative period is, thus, crucial for the patients' safety. We propose an automatic face-analysis approach to detect morphological traits related to difficult intubation and improve its prediction.  For this purpose, we have collected a database of 970 patients including photos, videos, and ground truth data. Specific statistical face models have been learned using the faces in our database providing an automated parametrization of the facial morphology. The most discriminative morphological features are selected through the importance ranking provided by the random forest algorithm. The random forest approach has also been used to train a classifier on these selected features. We compare a threshold tuning method based on class prior with two methods, which learn an optimal threshold on a training set for tackling the inherent imbalanced nature of the database.  Our fully automated method achieves an AUC of 81.0% in a simplified experimental setup, where only easy and difficult patients are considered. A further validation on the entire database has proven that our method is applicable for real-world difficult intubation prediction, with AUC = 77.9%.  The system performance is in line with the state-of-the-art medical diagnosis, based on ratings provided by trained anesthesiologists, whose assessment is guided by an extensive set of criteria.  We present the first completely automatic and noninvasive difficult intubation detection system that is suitable for use in clinical settings.
Segmentation of Head and Neck Lymph Node Regions for Radiotherapy Planning Using Active Contour-Based Atlas Registration

In this paper, we present the segmentation of the head and neck lymph node regions using a new active contour-based atlas registration model. We propose to segment the lymph node regions without directly including them in the atlas registration process; instead, they are segmented using the dense deformation field computed from the registration of the atlas structures with distinct boundaries. This approach results in robust and accurate segmentation of the lymph node regions even in the presence of significant anatomical variations between the atlas-image and the patient's image to be segmented. We also present a quantitative evaluation of lymph node regions segmentation using various statistical as well as geometrical metrics: sensitivity, specificity, dice similarity coefficient and Hausdorff distance. A comparison of the proposed method with two other state of the art methods is presented. The robustness of the proposed method to the atlas selection, in segmenting the lymph node regions, is also evaluated.
Single-FPGA, scalable, low-power, and high-quality 3D ultrasound beamformer

We present an efficient FPGA architecture suitable for a medical 3D ultrasound beamformer. We tackle the delay calculation bottleneck, which is the heart and the most critical part of the beamformer, by proposing a computationally efficient design that is able to perform volumetric real-time beamforming on a single-chip FPGA. The design has been demonstrated for a 32×32-channel receive probe, and we extrapolated the requirements of the architecture for 80×80 channels.
Atlas-based segmentation of medical images locally constrained by level sets

Atlas-based segmentation has become a standard paradigm for exploiting prior knowledge in medical image segmentation. In this paper, we propose a method to exploit both the robustness of global registration techniques and the accuracy of a local registration based on level set tracking. First, the atlas is globally put in correspondence with the patient image by an affine and an intensity-based non rigid registration. Based on this rough initialisation, the level set functions corresponding to particular objects of interest of the deformed atlas are used to segment the corresponding objects in the patient image. We propose a technique to derive a dense deformation field from the motion of these level set functions. This is particularly important when we want to infer the position of invisible structures like the brain sub-thalamic nuclei from the position of visible surrounding structures. This can also be advantageously exploited to register an atlas following a hierarchical approach. Results are shown on 2D synthetic images and 2D real images extracted from brain and prostate MR volumes and neck CT volumes.
Object-based coding of volumetric medical data

Medical data are increasingly represented as volumes. The huge amount of data to be handled every day in clinical practice makes compression an unavoidable step in picture archiving and communication systems (PACS). We propose an object-based coding scheme for 3D datasets, providing full range quality scalability for each semantically meaningful region. An integer three-dimensional separable wavelet transform is performed on the whole volume. An analytic model based on hybrid ellipsoids is used to describe the different objects, which are then coded independently allowing random access. The adopted coding strategy is the 3D extension of the well known EZW approach. First results show that 3D EZW outperforms both JPEG and the bi-dimensional version of the same coding algorithm in both lossless and lossy modes.
Demo: Efficient delay and apodization for on-FPGA 3D ultrasound

In medical diagnosis, ultrasound (US) imaging is one of the most common, safe, and powerful techniques. Volumetric (3D) US is potentially very attractive, compared to 2D US, because it might enable telesonography - decoupling the local image acquisition, by an untrained person, and the diagnosis, by the trained sonographer, who can be remote. Unfortunately, current 3D systems are hospital-oriented, bulky and expensive, and they cannot be available in emergency operations or rural areas. This motivates us to develop a portable US platform with cheap, battery-operated, more efficient electronics.
Sparsity in tensor optimization for optical-interferometric imaging

Image recovery in optical interferometry is an ill-posed nonlinear inverse problem arising from incomplete power spectrum and bispectrum measurements. We review our previous work, which reformulates this nonlinear problem in the framework of tensor recovery and studies two different approaches to solve it: one is nonlinear and nonconvex while the other is linear and convex. We extend the linear convex procedure to account for signal sparsity and we also present numerical simulations that show the improvement in the quality of reconstruction of sparse images when including a sparsity prior.