Lior Weizman

Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI

This paper presents an automatic method for the segmentation, internal classification and follow-up of optic pathway gliomas (OPGs) from multi-sequence MRI datasets. Our method starts with the automatic localization of the OPG and its core with an anatomical atlas followed by a binary voxel classification with a probabilistic tissue model whose parameters are estimated from the MR images. The method effectively incorporates prior location, tissue characteristics, and intensity information for the delineation of the OPG boundaries in a consistent and repeatable manner. Internal classification of the segmented OPG volume is then obtained with a robust method that overcomes grey-level differences between learning and testing datasets. Experimental results on 25 datasets yield a mean surface distance error of 0.73 mm as compared to manual segmentation by experienced radiologists. Our method exhibits reliable performance in OPG growth follow-up MR studies, which are crucial for monitoring disease progression. To the best of our knowledge, this is the first method that addresses automatic segmentation, internal classification, and follow-up of OPG.

Compressed sensing for longitudinal MRI: An adaptive-weighted approach

PURPOSE Repeated brain MRI scans are performed in many clinical scenarios, such as follow up of patients with tumors and therapy response assessment. In this paper, the authors show an approach to utilize former scans of the patient for the acceleration of repeated MRI scans. METHODS The proposed approach utilizes the possible similarity of the repeated scans in longitudinal MRI studies. Since similarity is not guaranteed, sampling and reconstruction are adjusted during acquisition to match the actual similarity between the scans. The baseline MR scan is utilized both in the sampling stage, via adaptive sampling, and in the reconstruction stage, with weighted reconstruction. In adaptive sampling, k-space sampling locations are optimized during acquisition. Weighted reconstruction uses the locations of the nonzero coefficients in the sparse domains as a prior in the recovery process. The approach was tested on 2D and 3D MRI scans of patients with brain tumors. RESULTS The longitudinal adaptive compressed sensing MRI (LACS-MRI) scheme provides reconstruction quality which outperforms other CS-based approaches for rapid MRI. Examples are shown on patients with brain tumors and demonstrate improved spatial resolution. Compared with data sampled at the Nyquist rate, LACS-MRI exhibits signal-to-error ratio (SER) of 24.8 dB with undersampling factor of 16.6 in 3D MRI. CONCLUSIONS The authors presented an adaptive method for image reconstruction utilizing similarity of scans in longitudinal MRI studies, where possible. The proposed approach can significantly reduce scanning time in many applications that consist of disease follow-up and monitoring of longitudinal changes in brain MRI.

Urban-Area Segmentation Using Visual Words

In this letter, we address the problem of urban-area extraction by using a feature-free image representation concept known as ldquoVisual Words.rdquo This method is based on building a ldquodictionaryrdquo of small patches, some of which appear mainly in urban areas. The proposed algorithm is based on a new pixel-level variant of visual words and is based on three parts: building a visual dictionary, learning urban words from labeled images, and detecting urban regions in a new image. Using normalized patches makes the method more robust to changes in illumination during acquisition time. The improved performance of the method is demonstrated on real satellite images from three different sensors: LANDSAT, SPOT, and IKONOS. To assess the robustness of our method, the learning and testing procedures were carried out on different and independent images.

Target Detection and Verification via Airborne Hyperspectral and High-Resolution Imagery Processing and Fusion

Remote sensing is often used for detection of predefined targets, such as vehicles, man-made objects, or other specified objects. We describe a new technique that combines both spectral and spatial analysis for detection and classification of such targets. Fusion of data from two sources, a hyperspectral cube and a high-resolution image, is used as the basis of this technique. Hyperspectral imagers supply information about the physical properties of an object while suffering from low spatial resolution. The use of high-resolution imagers enables high-fidelity spatial analysis in addition to the spectral analysis. This paper presents a detection technique accomplished in two steps: anomaly detection based on the spectral data and the classification phase, which relies on spatial analysis. At the classification step, the detection points are projected on the high-resolution images via registration algorithms. Then each detected point is classified using linear discrimination functions and decision surfaces on spatial features. The two detection steps possess orthogonal information: spectral and spatial. At the spectral detection step, we want very high probability of detection, while at the spatial step, we reduce the number of false alarms. Thus, we obtain a lower false alarm rate for a given probability of detection, in comparison to detection via one of the steps only. We checked the method over a few tens of square kilometers, and here we present the system and field test results.

Vessels-Cut: a graph based approach to patient-specific carotid arteries modeling

We present a nearly automatic graph-based segmentation method for patient specific modeling of the aortic arch and carotid arteries from CTA scans for interventional radiology simulation. The method starts with morphological-based segmentation of the aorta and the construction of a prior intensity probability distribution function for arteries. The carotid arteries are then segmented with a graph min-cut method based on a new edge weights function that adaptively couples the voxel intensity, the intensity prior, and geometric vesselness shape prior. Finally, the same graph-cut optimization framework is used for nearly automatic removal of a few vessel segments and to fill minor vessel discontinuities due to highly significant imaging artifacts. Our method accurately segments the aortic arch, the left and right subclavian arteries, and the common, internal, and external carotids and their secondary vessels. It does not require any user initialization, parameters adjustments, and is relatively fast (150–470 secs). Comparative experimental results on 30 carotid arteries from 15 CTAs from two medical centres manually segmented by expert radiologist yield a mean symmetric surface distance of 0.79mm (std=0.25mm). The nearly automatic refinement requires about 10 seed points and took less than 2mins of treating physician interaction with no technical support for each case.

MRI internal segmentation of optic pathway gliomas: clinical implementation of a novel algorithm

PurposeOptic pathway gliomas (OPGs) are diagnosed based on typical MR features and require careful monitoring with serial MRI. Reliable, serial radiological comparison of OPGs is a difficult task, where accuracy becomes very important for clinical decisions on treatment initiation and results. Current radiological methodology usually includes linear measurements that are limited in terms of precision and reproducibility.MethodWe present a method that enables semiautomated segmentation and internal classification of OPGs using a novel algorithm. Our method begins with co-registration of the different sequences of an MR study so that T1 and T2 slices are realigned. The follow-up studies are then re-sliced according to the baseline study. The baseline tumor is segmented, with internal components classified into solid non-enhancing, solid-enhancing, and cystic components, and the volume is calculated. Tumor demarcation is then transferred onto the next study and the process repeated. Numerical values are correlated with clinical data such as treatment and visual ability.ResultsWe have retrospectively implemented our method on 24 MR studies of three OPG patients. Clinical case reviews are presented here. The volumetric results have been correlated with clinical data and their implications are also discussed.ConclusionsThe heterogeneity of OPGs, the long course, and the young age of the patients are all driving the demand for more efficient and accurate means of tumor follow-up. This method may allow better understanding of the natural history of the tumor and provide a more advanced means of treatment evaluation.

Automatic segmentation and components classification of optic pathway gliomas in MRI

We present a new method for the automatic segmentation and components classification of brain Optic Pathway Gliomas (OPGs) from multi-spectral MRI datasets. Our method accurately identifies the sharp OPG boundaries and consistently delineates the missing contours by effectively incorporating prior location, shape, and intensity information. It then classifies the segmented OPG volume into its three main components--solid, enhancing, and cyst--with a probabilistic tumor tissue model generated from training datasets that accounts for the datasets grey-level differences. Experimental results on 25 datasets yield a mean OPG boundary surface distance error of 0.73mm and mean volume overlap difference of 30.6% as compared to manual segmentation by an expert radiologist. A follow-up patient study shows high correlation between the clinical tumor progression evaluation and the component classification results. To the best of our knowledge, ours is the first method for automatic OPG segmentation and component classification that may support quantitative disease progression and treatment efficacy evaluation.

Reference-based compressed sensing: A sample complexity approach

We address the problem of reference-based compressed sensing: reconstruct a sparse signal from few linear measurements using as prior information a reference signal, a signal similar to the signal we want to reconstruct. Access to reference signals arises in applications such as medical imaging, e.g., through prior images of the same patient, and compressive video, where previously reconstructed frames can be used as reference. Our goal is to use the reference signal to reduce the number of required measurements for reconstruction. We achieve this via a reweighted ℓ1-ℓ1 minimization scheme that updates its weights based on a sample complexity bound. The scheme is simple, intuitive and, as our experiments show, outperforms prior algorithms, including reweighted ℓ1 minimization, ℓ1-ℓ1 minimization, and modified CS.

The effect of chemotherapy on optic pathway gliomas and their sub-components: A volumetric MR analysis study

Optic pathway gliomas (OPG) represent 5% of pediatric brain tumors and compose a major therapeutic dilemma to the treating physicians. While chemotherapy is widely used for these tumors, our ability to predict radiological response is still lacking. In this study, we use volumetric imaging to examine in detail the long‐term effect of chemotherapy on the tumor as well as its various sub‐components.

Classification of hyperspectral remote-sensing images using discriminative linear projections

In this study we apply a variant of a recently proposed linear subspace method, the Neighbourhood Component Analysis (NCA), to the task of hyperspectral classification. The NCA algorithm explicitly utilizes the classification performance criterion to obtain the optimal linear projection. NCA assumes nothing about the form of each class and the shape of the separating surfaces. In some cases we would like to weight the penalty function for different types of misclassifications of the algorithm. A modification of the NCA cost function is introduced for this case. Experimental studies are conducted on hyperspectral images acquired by two sensors: the Airborne Visible/Infrared Imaging Spectroradiometer (AVIRIS) and AISA-EAGLE. Experimental results confirm the superiority of the NCA classifier in the context of hyperspectral data classification over methodologies that were previously suggested.