Abouzar Eslami

Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography

Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CTA) has rapidly emerged, and is nowadays widely used in clinical practice. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms devised to detect and quantify the coronary artery stenoses, and to segment the coronary artery lumen in CTA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (1) (semi-)automatically detect and quantify stenosis on CTA, in comparison with quantitative coronary angiography (QCA) and CTA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CTA, in comparison with experts manual annotation. A database consisting of 48 multicenter multivendor cardiac CTA datasets with corresponding reference standards are described and made available. The algorithms from 11 research groups were quantitatively evaluated and compared. The results show that (1) some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that (2) automatic lumen segmentation is possible with a precision similar to that obtained by experts. The framework is open for new submissions through the website, at http://coronary.bigr.nl/stenoses/.

Segmentation by retrieval with guided random walks: Application to left ventricle segmentation in MRI

In this paper, a new segmentation framework with prior knowledge is proposed and applied to the left ventricles in cardiac Cine MRI sequences. We introduce a new formulation of the random walks method, coined as guided random walks, in which prior knowledge is integrated seamlessly. In comparison with existing approaches that incorporate statistical shape models, our method does not extract any principal model of the shape or appearance of the left ventricle. Instead, segmentation is accompanied by retrieving the closest subject in the database that guides the segmentation the best. Using this techniques, rare cases can also effectively exploit prior knowledge from few samples in training set. These cases are usually disregarded in statistical shape models as they are outnumbered by frequent cases (effect of class population). In the worst-case scenario, if there is no matching case in the database to guide the segmentation, performance of the proposed method reaches to the conventional random walks, which is shown to be accurate if sufficient number of seeds is provided. There is a fast solution to the proposed guided random walks by using sparse linear matrix operations and the whole framework can be seamlessly implemented in a parallel architecture. The method has been validated on a comprehensive clinical dataset of 3D+t short axis MR images of 104 subjects from 5 categories (normal, dilated left ventricle, ventricular hypertrophy, recent myocardial infarction, and heart failure). The average segmentation errors were found to be 1.54 mm for the endocardium and 1.48 mm for the epicardium. The method was validated by measuring different algorithmic and physiologic indices and quantified with manual segmentation ground truths, provided by a cardiologist.

Iterative Self-Organizing Atherosclerotic Tissue Labeling in Intravascular Ultrasound Images and Comparison With Virtual Histology

Intravascular ultrasound (IVUS) is the predominant imaging modality in the field of interventional cardiology that provides real-time cross-sectional images of coronary arteries and the extent of atherosclerosis. Due to heterogeneity of lesions and stringent spatial/spectral behavior of tissues, atherosclerotic plaque characterization has always been a challenge and still is an open problem. In this paper, we present a systematic framework from in vitro data collection, histology preparation, IVUS-histology registration along with matching procedure, and finally a robust texture-derived unsupervised atherosclerotic plaque labeling. We have performed our algorithm on in vitro and in vivo images acquired with single-element 40 MHz and 64-elements phased array 20 MHz transducers, respectively. In former case, we have quantified results by local contrasting of constructed tissue colormaps with corresponding histology images employing an independent expert and in the latter case, virtual histology images have been utilized for comparison. We tackle one of the main challenges in the field that is the reliability of tissues behind arc of calcified plaques and validate the results through a novel random walks framework by incorporating underlying physics of ultrasound imaging. We conclude that proposed framework is a formidable approach for retrieving imperative information regarding tissues and building a reliable training dataset for supervised classification and its extension for in vivo applications.

Surgical Tool Tracking and Pose Estimation in Retinal Microsurgery

Retinal Microsurgery RM is performed with small surgical tools which are observed through a microscope. Real-time estimation of the tools pose enables the application of various computer-assisted techniques such as augmented reality, with the potential of improving the clinical outcome. However, most existing methods are prone to fail in in-vivo sequences due to partial occlusions, illumination and appearance changes of the tool. To overcome these problems, we propose an algorithm for simultaneous tool tracking and pose estimation that is inspired by state-of-the-art computer vision techniques. Specifically, we introduce a method based on regression forests to track the tool tip and to recover the tools articulated pose. To demonstrate the performance of our algorithm, we evaluate on a dataset which comprises four real surgery sequences, and compare with the state-of-the-art methods on a publicly available dataset.

IVUS-histology image registration

In this paper, for the first time, we present a systematic framework to register intravascular ultrasound (IVUS) images with histology correspondences. We deployed intermediate representations of images, generating segmentation masks corresponding to lumen and media-adventitia borders for both histology and IVUS images, incorporated into a non-rigid registration framework using discrete multi-labeling and approximate curvature penalty for smoothness regularization. The resulting deformation field was then applied to the original histology image to transfer it to IVUS coordinate system. Finally, the results were quantified on 14 cross sections of interest. The main contribution of this work is that the registered results could be used for systematic labeling of tissues, which ultimately will lead to reliable construction of training dataset for feature extraction and supervised classification of atherosclerotic tissues.

Joint learning of ultrasonic backscattering statistical physics and signal confidence primal for characterizing atherosclerotic plaques using intravascular ultrasound.

Intravascular Ultrasound (IVUS) is a predominant imaging modality in interventional cardiology. It provides real-time cross-sectional images of arteries and assists clinicians to infer about atherosclerotic plaques composition. These plaques are heterogeneous in nature and constitute fibrous tissue, lipid deposits and calcifications. Each of these tissues backscatter ultrasonic pulses and are associated with a characteristic intensity in B-mode IVUS image. However, clinicians are challenged when colocated heterogeneous tissue backscatter mixed signals appearing as non-unique intensity patterns in B-mode IVUS image. Tissue characterization algorithms have been developed to assist clinicians to identify such heterogeneous tissues and assess plaque vulnerability. In this paper, we propose a novel technique coined as Stochastic Driven Histology (SDH) that is able to provide information about co-located heterogeneous tissues. It employs learning of tissue specific ultrasonic backscattering statistical physics and signal confidence primal from labeled data for predicting heterogeneous tissue composition in plaques. We employ a random forest for the purpose of learning such a primal using sparsely labeled and noisy samples. In clinical deployment, the posterior prediction of different lesions constituting the plaque is estimated. Folded cross-validation experiments have been performed with 53 plaques indicating high concurrence with traditional tissue histology. On the wider horizon, this framework enables learning of tissue-energy interaction statistical physics and can be leveraged for promising clinical applications requiring tissue characterization beyond the application demonstrated in this paper.

Hunting for necrosis in the shadows of intravascular ultrasound.

Coronary artery disease leads to failure of coronary circulation secondary to accumulation of atherosclerotic plaques. In adjunction to primary imaging of such vascular plaques using coronary angiography or alternatively magnetic resonance imaging, intravascular ultrasound (IVUS) is used predominantly for diagnosis and reporting of their vulnerability. In addition to plaque burden estimation, necrosis detection is an important aspect in reporting of IVUS. Since necrotic regions generally appear as hypoechic, with speckle appearance in these regions resembling true shadows or severe signal dropout regions, it contributes to variability in diagnosis. This dilemma in clinical assessment of necrosis imaged with IVUS is addressed in this work. In our approach, fidelity of the backscattered ultrasonic signal received by the imaging transducer is initially estimated. This is followed by identification of true necrosis using statistical physics of ultrasonic backscattering. A random forest machine learning framework is used for the purpose of learning the parameter space defining ultrasonic backscattering distributions related to necrotic regions and discriminating it from non-necrotic shadows. Evidence of hunting down true necrosis in shadows of intravascular ultrasound is presented with ex vivo experiments along with cross-validation using ground truth obtained from histology. Nevertheless, in some rare cases necrosis is marginally over-estimated, primarily on account of non-reliable statistics estimation. This limitation is due to sparse spatial sampling between neighboring scan-lines at location far from the transducer. We suggest considering the geometrical location of detected necrosis together with estimated signal confidence during clinical decision making in view of such limitation.

Supervised classification for customized intraoperative augmented reality visualization

In this paper, we present a fusion algorithm supplemented with appropriate visualization by selecting relevant information from different modalities in mixed and augmented reality (AR). This encompasses a learning based method upon relevance of information, defined by an expert, which ultimately enables confident interventional decisions based on mixed reality (MR) images. The performance of our developed fusion and tailored visualization techniques was evaluated by employing X-ray/optical images during surgery and validated qualitatively using a 5-point Likert scale. Our observations indicated that the proposed technique provided semantic contextual information about underlying pixels and in general was preferred over the traditional pixel-wise linear alpha-blending method.

Real-time localization of articulated surgical instruments in retinal microsurgery

Real-time visual tracking of a surgical instrument holds great potential for improving the outcome of retinal microsurgery by enabling new possibilities for computer-aided techniques such as augmented reality and automatic assessment of instrument manipulation. Due to high magnification and illumination variations, retinal microsurgery images usually entail a high level of noise and appearance changes. As a result, real-time tracking of the surgical instrument remains challenging in in-vivo sequences. To overcome these problems, we present a method that builds on random forests and addresses the task by modelling the instrument as an articulated object. A multi-template tracker reduces the region of interest to a rectangular area around the instrument tip by relating the movement of the instrument to the induced changes on the image intensities. Within this bounding box, a gradient-based pose estimation infers the location of the instrument parts from image features. In this way, the algorithm does not only provide the location of instrument, but also the positions of the tool tips in real-time. Various experiments on a novel dataset comprising 18 in-vivo retinal microsurgery sequences demonstrate the robustness and generalizability of our method. The comparison on two publicly available datasets indicates that the algorithm can outperform current state-of-the art.

Deformable Registration of Multi-modal Microscopic Images Using a Pyramidal Interactive Registration-Learning Methodology

Co-registration of multi-modal microscopic images can integrate benefits of each modality, yet major challenges come from inherent difference between staining, distortions of specimens and various artefacts. In this paper, we propose a new interactive registration-learning method to register functional fluorescence (IF) and structural histology (HE) images in a pyramidal fashion. We synthesize HE image from the multi-channel IF image using a supervised machine learning technique and hence reduce the multi-modality registration problem into a mono-modality one, in which case the normalised cross correlation is used as the similarity measure. Unlike conventional applications of supervised learning, our classifier is not trained by ‘ground-truth’ (perfectly-registered) training dataset, as they are not available. Instead, we use a relatively noisy training dataset (affinely-registered) as an initialization and rely on the robustness of machine learning to the outliers and label updates via pyramidal deformable registration to gain better learning and predictions. In this sense, the proposed methodology has potential to be adapted in other learning problems as the manual labelling is usually imprecise and very difficult in the case of heterogeneous tissues.