nils-herrmann/clean_scirepeval huggingface dataset:
Modeling a parallelism constraint in active contours. Application to the segmentation of eye vessels and retinal layers

Parametric deformable models are an important technique for image segmentation. In order to improve the robustness of the model, it may be interesting to incorporate a priori information about the shape of the objects to be segmented. In this paper, we propose to add a parallelism constraint. Such a model is relevant in many applications where elongated structures have to be detected. One main advantage of our formulation is that it only needs few parameters to be adjusted in addition to those of traditional snakes. The proposed model has been applied for the segmentation of OCT images of the retina and for the segmentation of retinal vessels. Experimental results, obtained on 25 OCT images and 30 eye fundus images, demonstrated the robustness, flexibility and large potential applicability of this new formulation. The accuracy of the method has been assessed by comparing manual segmentations, made by experts, with the automatic ones.
From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning

Brain magnetic resonance imaging (MRI) is widely used to assess brain development in neonates and to diagnose a wide range of neurological diseases in adults. Such studies are usually based on quantitative analysis of different brain tissues, so it is essential to be able to classify them accurately. In this paper, we propose a fast automatic method that segments 3D brain MR images into different tissues using fully convolutional network (FCN) and transfer learning. As compared to existing deep learning-based approaches that rely either on 2D patches or on fully 3D FCN, our method is way much faster: it only takes a few seconds, and only a single modality (T1 or T2) is required. In order to take the 3D information into account, all 3 successive 2D slices are stacked to form a set of 2D “color” images, which serve as input for the FCN pre-trained on ImageNet for natural image classification. To the best of our knowledge, this is the first method that applies transfer learning to segment both neonatal and adult brain 3D MR images. Our experiments on two public datasets show that our method achieves state-of-the-art results.
Joint variational segmentation of CT-PET data for tumoral lesions

Medical imaging, used for both diagnosis and therapy planning, is evolving towards multi-modality acquisition protocols. Manual segmentation of 3D images is a tedious task and prone to inter- and inter-experts variability. Moreover, the automatic segmentation exploiting the characteristics of multi-modal images is still a difficult problem. In this paper, we propose the use of a variational segmentation method, based on the minimization of the TV norm and a convex formulation, for segmenting thoracic pairs of PET and CT images, in the context of radiotherapy planning. We first highlight the limitations of a pure vectorial formulation of the variational segmentation method for PET and CT images. We then propose to better exploit the bi-modality by introducing a parameter which varies spatially depending on the PET intensity to adjust precisely the segmentation of CT images. Segmentation results on lung tumors and lymphatic nodes are shown, and comparisons performed with manual segmentations illustrate the quality of the results.
Brain symmetry plane computation in MR images using inertia axes and optimization

Detection of the best symmetry plane in 3D images can be treated as a registration problem between the original and the reflected images. The registration is performed in a 3D space of parameters defining orientation and shift of a reflection plane. We use the normalized l/sub 2/ metric as the similarity measure between original and reflected images and investigate an algorithm for computation of the best symmetry plane. The algorithm computes first an initial position of the plane by analyzing principal inertia axes. We demonstrate on several MR brain images that the initial position is in the neighborhood of the global maximum. Therefore the downhill simplex method is further used for the computation of the best symmetry plane. The proposed algorithm was tested on simulated and real MR brain images.
Automated segmentation of retinal layers in OCT imaging and derived ophthalmic measures

This paper proposes an automated method for the segmentation of eight retinal layers in high resolution OCT images. It has been evaluated based on comparison with manual segmentation performed by five different experts. The method has been successfully applied on a database of 72 images. Quantitative measures are then derived as an aid to ophthalmic diagnosis. A good agreement with measures derived from manual segmentation is obtained which allows us to use the proposed method for retinal variability studies.
Segmenting internal structures in 3D MR images of the brain by Markovian relaxation on a watershed based adjacency graph

The authors present a fast stochastic method aiming at segmenting cerebral internal structures in 3D magnetic resonance images. An original method introducing context permits the authors to obtain reliable radiometric characteristics even for hardly discriminable brain structures. Segmentation is formulated as the labeling of a region adjacency graph. The graph is constructed by an extension to 3D of the watershed algorithm and the labeling is performed using a Markovian relaxation process. This leads to consistent results with a very low computational burden.
Multiple template deformation application to abdominal organ segmentation

We propose a fast, automatic and versatile framework for the segmentation of multiple anatomical structures from 2D and 3D images. We extend the work of  on implicit template deformation to multiple targets. Our variational formulation optimizes the non-rigid transformation of a set of templates according to image-driven forces. It embeds non-overlapping constraints ensuring a consistent segmentation result. We demonstrate the potential of our approach on the segmentation of abdominal organs (liver, kidneys, spleen and gallbladder) with an evaluation on CT volumes (50 for training and 50 for testing). Our method reaches state-of-the-art accuracy, ranging from 2mm (liver and kidneys) to 8mm (gallbladder).
Sensor fusion in anti-personnel mine detection using a two-level belief function model

A two-level approach for modeling and fusion of antipersonnel mine detection sensors in terms of belief functions within the Dempster-Shafer framework is presented. Three promising and complementary sensors are considered: a metal detector, an infrared camera, and a ground-penetrating radar. Since the metal detector, the most often used mine detection sensor, provides measures that have different behaviors depending on the metal content of the observed object, the first level aims at identifying this content and at providing a classification into three classes. Depending on the metal content, the object is further analyzed at the second level toward deciding the final object identity. This process can be applied to any problem where one piece of information induces different reasoning schemes depending on its value. A way to include influence of various factors on sensors in the model is also presented, as well as a possibility that not all sensors refer to the same object. An original decision rule adapted to this type of application is proposed, as well as a way for estimating confidence degrees. More generally, this decision rule can be used in any situation where the different types of errors do not have the same importance. Some examples of obtained results are shown on synthetic data mimicking reality and with increasing complexity. Finally, applications on real data show promising results.
Influence of the ear's morphology on specific absorption rate (SAR) induced in a child head using two source models.

In this paper, an investigation is elaborated for the Specific Absorption Rate (SAR) induced in the head of 12 years old children having different ear dimensions. Two types of sources are employed : a mobile phone and a dipole. Both the average SAR over a mass of 10g and 1g in the head and the power budget are determined in each tissue using the Finite Difference Time Domain (FDTD) method. The differences between the results for different ear dimensions are given at 900MHz and 1800MHz. The mean and standard deviation about the SAR and power budget are also given for this age class. Index Terms — FDTD, Dosimetry, Child RF exposure, Mobile phone.
Alignment and Parallelism for the Description of High-Resolution Remote Sensing Images

Alignment and parallelism are frequently found between objects in high-resolution remote sensing images and can be used to interpret and describe the observed scenes. In this paper, we propose new representations of parallelism and alignment as fuzzy spatial relations, which capture the imprecision in the semantics of both relations. We propose two novel definitions of alignment between objects: local and global. In local alignment, each object of the group is aligned with its neighbors, while in global alignment, every object of the group is aligned to all other members. Both definitions consider each object as a whole and are based on relative position measures. They are robust with respect to segmentation errors. Furthermore, we propose an efficient graph-based method to determine which are the locally and the globally aligned groups of objects from a set of segmented objects. In addition, we propose a fuzzy definition for the parallel relation, which is also based on relative position measures and is adequate to represent the parallelism between a globally aligned group of objects and another object or group of objects. Illustrative examples on optical satellite images show the description power of these two relations and their combination for image interpretation.
Characterization of mine detection sensors in terms of belief functions and their fusion, first results

In this paper, characterization of mine detection sensors in terms of belief functions and their fusion are presented. The need for fusion of mine detection sensors is discussed, and reasons for choosing Dempster-Shafer framework are pointed out, taking into account specificity and sensitivity of the problem. This work is done in the scope of the HUDEM project, where three promising and complementary sensors are under analysis. These sensors are presented, and detail analysis is performed in case of fusing the data from them. A way for including in the model influence of various factors on sensors and their results is discussed as well and will be further analyzed in the future. The application of the approach proposed in this paper is illustrated on the frequent case of detecting metallic objects, but the possibility for modifying it to some other situations exists.
Interest of the Steady State Free Precession (SSFP) sequence for 3D modeling of the whole fetus

Fetal magnetic resonance imaging (MRI) has been gaining interest over the last two decades. Current fast MRI sequences provide imaging data of the whole uterus in less than 20 seconds, avoiding fetal motion related artifacts without any maternal or fetal sedation. MRI has proved to be a useful adjunct to echographic screening for prenatal diagnosis. However, MRI volumetric data is still mainly interpreted on 2D slices and 3D applications remain limited. In this paper, we discuss the qualities of the SSFP MRI sequences to provide adequate data for 3D segmentation and modeling of the fetus. Potential exploitations of 3D segmentation and derived anatomical models cover several domains: biometric and morphologic clinical studies, quantitative longitudinal studies of normal and abnormal fetus developments, direct visualization of the overall fetus body and simulations in different fields (surgery, radiation dosimetry,...).
Contrast enhancement of Micro Dose X-ray images

A multi-scale (MS) decomposition method for contrast enhancement of Micro Dose (MD) X-ray images is presented in this paper. First, we get a denoised version of the input exploiting a non-local means filter with adaptable parameters setting that we defined in a former approach. Then, the MS representations of the input and of its de-noised version are combined to obtain an optimal image in terms of preservation of details and noise attenuation. The efficiency of the algorithm is demonstrated by quantitative and qualitative assessments on both phantoms and clinical MD images.
A first step toward automatic interpretation of SAR images using evidential fusion of several structure detectors

The authors propose a method aiming to characterize the spatial organization of the main cartographic elements of a synthetic aperture radar (SAR) image and thus giving an almost automatic interpretation of the scene. Their approach is divided into three main steps which build the whole image interpretation gradually. The first step consists of applying low-level detectors taking the speckle statistics into account and extracting some raw information from the scene. The detector responses are then fused in a second step using Dempster-Shafer theory, thus allowing the modeling of the knowledge that there is about operators, including possible ignorance and their limits. A third step gives the final image interpretation using contextual knowledge between the different classes. Results of the whole method applied to different SAR images and to various landscapes are presented.
An automated myocardial segmentation in cardiac MRI

In this paper we present an automatic approach to segment cardiac magnetic resonance (CMR) images. A preprocessing step that consists in filtering the image using connected operators (area opening and closing filters) is applied in order to homogenize the cavity and solve the problems due to the papillary muscles. Thereby the GVF snake algorithm is applied with one point clicked in the cavity as initialization and an optimized tuning of parameters for the endocardial contour extraction. The epicardial border is then obtained using the endocardium as initialization. The performance of the proposed method was assessed by experimentation on thirty- nine CMR images. A high agreement between manual and automatic contours was obtained with correlation scores of 0.96 for the endocardium and 0.90 for the epicardium. Overlapping percentage, mean and maximum distances between the two contours show a good performance of the method.
Integration of Fuzzy Spatial Information in Tracking Based on Particle Filtering

In this paper, we propose a novel method to introduce spatial information in particle filters. This information may be expressed as spatial relations (orientation, distance, etc.), velocity, scaling, or shape information. Spatial information is modeled in a generic fuzzy-set framework. The fuzzy models are then introduced in the particle filter and automatically define transition and prior spatial distributions. We also propose an efficient importance distribution to produce relevant particles, which is dedicated to the proposed fuzzy framework. The fuzzy modeling provides flexibility both in the semantics of information and in the transitions from one instant to another one. This allows one to take into account situations where a tracked object changes its direction in a quite abrupt way and where poor prior information on dynamics is available, as demonstrated on synthetic data. As an illustration, two tests on real video sequences are performed in this paper. The first one concerns a classical tracking problem and shows that our approach efficiently tracks objects with complex and unknown dynamics, outperforming classical filtering techniques while using only a small number of particles. In the second experiment, we show the flexibility of our approach for modeling: Fuzzy shapes are modeled in a generic way and allow the tracking of objects with changing shape.
Enhancing Electronic Patient Record Functionality Through Information Extraction from Images

Complex multimedia medical information is being increasingly used to enable diagnosis. Despite the recognized potential of emerging exploitation of such medical information, known electronic patient record applications do not offer the possibility to make use of them. In order to examine the interest of handling complementary medical information, this work focuses on the integration into the electronic patient record of segmented lesions (e.g. tumors) and prior anatomical knowledge, in a neurological examination scenario. Based on a generic architecture, the proposed application proposes a rich and flexible graphic user interface, capable of simultaneously displaying 2D and 3D images along with segmented lesions, associated to anatomo-functional knowledge. Open source technology was chosen for prototype implementation. The main modules are described and analyzed
An automated quantification of the transmural myocardial infarct extent using cardiac DE-MR images

Evaluating myocardial viability is an important prognostic factor in the follow-up of infarctions. Delayed Enhancement magnetic resonance (DE-MR) imaging allows precise delineation of the infarct transmural extent. Visual interpretation is the most commonly used method to assess the myocardial infarction (MI) transmural extent. This study proposes to automate the segmentation of the (DE) images prior to the estimation of the extent of infarcted tissue. Indeed the segmentation of the myocardium was performed using cine contraction images which present a high contrast between cavity and myocardium. After the segmentation, the segmental transmurality is estimated on a conventional five point scale. A head to head comparison was performed between visual and quantitative analysis of infarct transmurality on DE-MR imaging. Results on 921 sub-segments (9 patients) showed an absolute agreement of 80% and a relative agreement (with one point difference) of 97%.
A challenging issue: Detection of white matter hyperintensities in neonatal brain MRI.

The progress of magnetic resonance imaging (MRI) allows for a precise exploration of the brain of premature infants at term equivalent age. The so-called DEHSI (diffuse excessive high signal intensity) of the white matter of premature brains remains a challenging issue in terms of definition, and thus of interpretation. We propose a semi-automatic detection and quantification method of white matter hyperintensities in MRI relying on morphological operators and max-tree representations, which constitutes a powerful tool to help radiologists to improve their interpretation. Results show better reproducibility and robustness than interactive segmentation.
Segmentation of Retinal Arteries in Adaptive Optics Images

In this paper, we present a method for automatically segmenting the walls of retinal arteries in adaptive optics images. To the best of our knowledge, this is the first method addressing this problem in such images. To achieve this goal, we propose to model these walls as four curves approximately parallel to a common reference line located near the center of vessels. Once this line is detected, the curves are simultaneously positioned as close as possible to the borders of walls using an original tracking procedure to cope with deformations along vessels. Then, their positioning is refined using a deformable model embedding a parallelism constraint. Such an approach enables us to control the distance of the curves to their reference line and improve the robustness to image noise. This model was evaluated on healthy subjects by comparing the results against segmentations from physicians. Noticeably, the error introduced by this model is smaller or very near the inter-physicians error.
A new method based on template registration and deformable models for pelvic bones semi-automatic segmentation in pediatric MRI

In this paper we address the problem of bone segmentation in MRI images of children, in the region of the pelvis. To cope with the complex structure of the bones in this region and their changing topology during growth, we propose a method relying on 3D bone templates. These models are built from 3D CT images. For a given MRI volume, the closest template is chosen and registered on the MRI data. This leads to an initial segmentation which is then refined using a deformable model approach, where the regularization parameters depend on the local curvature, and the landmarks used during the registration are fixed anchors during the deformation. This approach was successfully applied to 15 MRI volumes of children between 1 and 18 years old, with an average accuracy in terms of medium distance of MD = 1.17 ± 0.29mm and Dice Index of DC = 0.81 ± 0.04.
Subject-specific channel selection for classification of motor imagery electroencephalographic data

Brain-computer interfaces (BCIs) are systems that record brain signals and then classify them to generate computer commands. Keeping a minimal number of channels (electrodes) is essential for developing portable BCIs. Unlike existing methods choosing channels without optimization of time segment for classification, this work proposes a novel subject-specific channel selection method based on a criterion derived from Fisher's discriminant analysis to realize the parametrization of both time segment and channel positions. The experimental results show that the method can efficiently reduce the number of channels (from 118 channels to no more than 11), and shorten the training time, without a significant decrease of classification accuracy on a standard dataset.
Structural Image Segmentation with Interactive Model Generation

An image segmentation method based on structural pattern recognition is presented. Two graphs are generated from the image to be segmented. A model graph is generated from an oversegmentation of the image and from traces provided by the user. An input graph is generated from the oversegmented image. Image segmentation is then obtained by matching the input graph to the model graph. An objective function is defined and optimized using a new approach to find the most suitable clique of the corresponding association graph. The structural information encoded in the graphs leads to a robust segmentation performance even in the case of non-homogeneous textured regions. Successful experimental results obtained from real images are provided.
Possibilistic Versus Belief Function Fusion for Antipersonnel Mine Detection

Two approaches for combining humanitarian mine detection sensors are presented-one based on belief functions and the other one based on possibility theory. The approaches are described in parallel. First, different measures are extracted from the sensor data. Mass functions and possibility distributions are then derived from the measures based on prior information. After that, the combination of masses and the combination of possibility degrees are performed in two steps, on a separate sensor level and between the sensors. Combination operators are chosen to account for different characteristics of the sensors. The selection of the decision rules is discussed for both approaches. The proposed approaches are illustrated on a set of real mines and nondangerous objects, and promising results have been obtained.