Tatyana Ivanovska

An efficient level set method for simultaneous intensity inhomogeneity correction and segmentation of MR images.

Intensity inhomogeneity (bias field) is a common artefact in magnetic resonance (MR) images, which hinders successful automatic segmentation. In this work, a novel algorithm for simultaneous segmentation and bias field correction is presented. The proposed energy functional allows for explicit regularization of the bias field term, making the model more flexible, which is crucial in presence of strong inhomogeneities. An efficient minimization procedure, attempting to find the global minimum, is applied to the energy functional. The algorithm is evaluated qualitatively and quantitatively using a synthetic example and real MR images of different organs. Comparisons with several state-of-the-art methods demonstrate the superior performance of the proposed technique. Desirable results are obtained even for images with strong and complicated inhomogeneity fields and sparse tissue structures.

A level set based framework for quantitative evaluation of breast tissue density from MRI data.

Breast density is a risk factor associated with the development of breast cancer. Usually, breast density is assessed on two dimensional (2D) mammograms using the American College of Radiology (ACR) classification. Magnetic resonance imaging (MRI) is a non-radiation based examination method, which offers a three dimensional (3D) alternative to classical 2D mammograms. We propose a new framework for automated breast density calculation on MRI data. Our framework consists of three steps. First, a recently developed method for simultaneous intensity inhomogeneity correction and breast tissue and parenchyma segmentation is applied. Second, the obtained breast component is extracted, and the breast-air and breast-body boundaries are refined. Finally, the fibroglandular/parenchymal tissue volume is extracted from the breast volume. The framework was tested on 37 randomly selected MR mammographies. All images were acquired on a 1.5T MR scanner using an axial, T1-weighted time-resolved angiography with stochastic trajectories sequence. The results were compared to manually obtained groundtruth. Dices Similarity Coefficient (DSC) as well as Bland-Altman plots were used as the main tools for evaluation of similarity between automatic and manual segmentations. The average Dices Similarity Coefficient values were and for breast and parenchymal volumes, respectively. Bland-Altman plots showed the mean bias () standard deviation equal for breast volumes and for parenchyma volumes. The automated framework produced sufficient results and has the potential to be applied for the analysis of breast volume and breast density of numerous data in clinical and research settings.

Fast Implementations of the Levelset Segmentation Method With Bias Field Correction in MR Images: Full Domain and Mask-Based Versions

Intensity inhomogeneity represents a significant challenge in image processing. Popular image segmentation algorithms produce inadequate results in images with intensity inhomogeneity. Existing correction methods are often computationally expensive. Therefore, efficient implementations for the bias field estimation and inhomogeneity correction are required. In this work, we propose an extended mask-based version of the levelset method, recently presented by Li et al. [1]. We develop efficient CUDA implementations for the original full domain and the extended mask-based versions. We compare the methods in terms of speed, efficiency, and performance. Magnetic resonance (MR) images are one of the main application in practice.

A fast global variational bias field correction method for MR images

Magnetic resonance (MR) images are prone to inhomogeneity artefacts that hinder an efficient automatic segmentation. Existing correction methods are often dependent on initialization and computationally expensive. This paper proposes a novel variational approach for the simultaneous bias field correction and image segmentation together with its efficient implementation, which produces the global solution that does not depend on initializations. The method is compared against another recently proposed method in terms of speed, efficiency, and performance.

Pharynx Segmentation from MRI Data for Analysis of Sleep Related Disoders

In our project, soft tissue structures of a throat are examined via MRI and anatomic risk factors for sleep related disorders are studied. Segmentation of pharyngeal structures is the first step in three dimensional analysis of throat tissues. We present a pipeline for pharynx segmentation with semi-automatic initialization. The automatic part of the approach consists of three steps: smoothing, thresholding, and 2D and 3D connected component analysis. Whereas two first steps are rather common, the third step provides a set of general rules for extraction of the pharyngeal component. Our method is minimally interactive and requires less than one minute to extract the pharyngeal structures, including the operator interaction part. The approach is evaluated qualitatively using 6 data sets by measuring volume fractions and the Dices coefficient.

Visual Inspection and Error Detection in a Reconfigurable Robot Workcell: An Automotive Light Assembly Example

Small and medium size enterprises (SMEs) often have small batch production. It leads to decreasing product lifetimes and also to more frequent product launches. In order to assist such production, a highly reconfigurable robot workcell is being developed. In this work, a visual inspection system designed for the robot workcell is presented and discussed in the context of the automotive light assembly example. The proposed framework is implemented using ROS and OpenCV libraries. We describe the hardware and software components of the framework and explain the system’s benefits when compared to other commercial packages.

Lung Segmentation of MR Images: A Review

Magnetic resonance imaging (MRI) is a non-radiation based examination method, which gains an increasing popularity in research and clinical settings. Manual analysis of large data volumes is a very time-consuming and tedious process. Therefore, automatic analysis methods are required. This paper reviews different methods that have been recently proposed for automatic and semi-automatic lung segmentation from magnetic resonance imaging data. These techniques include thresholding, region growing, morphological operations, active contours, level sets, and neural networks. We also discuss the methodologies that have been utilized for performance and accuracy evaluation of each method.

Automatic Pharynx Segmentation from MRI Data for Obstructive Sleep Apnea Analysis

Obstructive sleep apnea (OSA) is a public health problem. Volumetric analysis of the upper airways can help us to understand the pathogenesis of OSA. A reliable pharynx segmentation is the first step in identifying the anatomic risk factors for this sleeping disorder. As manual segmentation is a time-consuming and subjective process, a fully automatic segmentation of pharyngeal structures is required when investigating larger data bases such as in cohort studies. We develop a context-based automatic algorithm for segmenting pharynx from magnetic resonance images (MRI). It consists of a pipeline of steps including pre-processing (thresholding, connected component analysis) to extract coarse 3D objects, classification of the objects (involving object-based image analysis (OBIA), visual feature space analysis, and silhouette coefficient computation) to segregate pharynx from other structures automatically, and post-processing to refine the shape of the identified pharynx (including extraction of the oropharynx and propagating results from neighboring slices to slices that are difficult to delineate). Our technique is fast such that we can apply our algorithm to population-based epidemiological studies that provide a high amount of data. Our method needs no user interaction to extract the pharyngeal structure. The approach is quantitatively evaluated on ten datasets resulting in an average of approximately 90% detected volume fraction and a 90% Dice coefficient, which is in the range of the interobserver variation within manual segmentation results.

Automatic Pharynx Segmentation from MRI Data for Analysis of Sleep Related Disorders

In our project, we analyse throat structures using magnetic resonance imaging (MRI) to associate anatomic risk factors with sleep related disorders. Pharynx segmentation is the first step in the three-dimensional analysis of throat tissues. We present a pipeline for automatic pharynx segmentation. The automatic part of the approach consists of three steps: smoothing, thresholding, 2D and 3D connected component analysis. Whereas two first steps are rather common, the third step provides a set of general rules for the automatic extraction of the pharyngeal component. Our method requires less than one minute to extract the pharyngeal structures. The approach is evaluated quantitatively on 30 data sets using region-based and edge-based measures.

Automated MR-based lung volume segmentation in population-based whole-body MR imaging: correlation with clinical characteristics, pulmonary function testing and obstructive lung disease

ObjectivesWhole-body MR imaging is increasingly utilised; although for lung dedicated sequences are often not included, the chest is typically imaged. Our objective was to determine the clinical utility of lung volumes derived from non-dedicated MRI sequences in the population-based KORA-FF4 cohort study.Methods400 subjects (56.4 ± 9.2 years, 57.6% males) underwent whole-body MRI including a coronal T1-DIXON-VIBE sequence in inspiration breath-hold, originally acquired for fat quantification. Based on MRI, lung volumes were derived using an automated framework and related to common predictors, pulmonary function tests (PFT; spirometry and pulmonary gas exchange, n = 214) and obstructive lung disease.ResultsMRI-based lung volume was 4.0 ± 1.1 L, which was 64.8 ± 14.9% of predicted total lung capacity (TLC) and 124.4 ± 27.9% of functional residual capacity. In multivariate analysis, it was positively associated with age, male, current smoking and height. Among PFT indices, MRI-based lung volume correlated best with TLC, alveolar volume and residual volume (RV; r = 0.57 each), while it was negatively correlated to FEV1/FVC (r = 0.36) and transfer factor for carbon monoxide (r = 0.16). Combining the strongest PFT parameters, RV and FEV1/FVC remained independently and incrementally associated with MRI-based lung volume (β = 0.50, p = 0.04 and β = – 0.02, p = 0.02, respectively) explaining 32% of the variability. For the identification of subjects with obstructive lung disease, height-indexed MRI-based lung volume yielded an AUC of 0.673–0.654.ConclusionLung volume derived from non-dedicated whole-body MRI is independently associated with RV and FEV1/FVC. Furthermore, its moderate accuracy for obstructive lung disease indicates that it may be a promising tool to assess pulmonary health in whole-body imaging when PFT is not available.Key Points• Although whole-body MRI often does not include dedicated lung sequences, lung volume can be automatically derived using dedicated segmentation algorithms• Lung volume derived from whole-body MRI correlates with typical predictors and risk factors of respiratory function including smoking and represents about 65% of total lung capacity and 125% of the functional residual capacity• Lung volume derived from whole-body MRI is independently associated with residual volume and the ratio of forced expiratory volume in 1 s to forced vital capacity and may allow detection of obstructive lung disease