K. S. Shriram

Automatic view classification of echocardiograms using Histogram of Oriented Gradients

When imaging the heart, using a 2D ultrasound probe, different views can manifest depending on the location and angulations of the probe. Some of these views have been labeled as standard views, due to the presentation and ease of assessment of key cardiac structures in them. We present an approach for automatic recognition and classification of these standard views - namely the Parasternal Long Axis (PLAX) and the Short Axis (SAX) B-mode echocardiograms. The Histogram of Oriented Gradients (HOG) used as the discriminating feature encodes the spatial arrangement of edges/gradients in the images. The HOG feature is computed on the pre-scan converted image data in the ultrasound beam space. On a fairly large database of 703 images, with a Support Vector Machine classifier we obtained an accuracy of about 98%.

Automatic tracking of neuro vascular tree paths

3-D analysis of blood vessels from volumetric CT and MR datasets has many applications ranging from examination of pathologies such as aneurysm and calcification to measurement of cross-sections for therapy planning. Segmentation of the vascular structures followed by tracking is an important processing step towards automating the 3-D vessel analysis workflow. This paper demonstrates a fast and automated algorithm for tracking the major arterial structures that have been previously segmented. Our algorithm uses anatomical knowledge to identify the start and end points in the vessel structure that allows automation. Voxel coding scheme is used to code every voxel in the vessel based on its geodesic distance from the start point. A shortest path based iterative region growing is used to extract the vessel tracks that are subsequently smoothed using an active contour method. The algorithm also has the ability to automatically detect bifurcation points of major arteries. Results are shown for tracking the major arteries such as the common carotid, internal carotid, vertebrals, and arteries coming off the Circle of Willis across multiple cases with various data related and pathological challenges from 7 CTA cases and 2 MR Time of Flight (TOF) cases.

Breast lesion detection and characterization with 3D features

Automated Breast Ultrasound (ABUS) is highly effective as breast cancer screening adjunct technology. Automation can greatly enhance the efficiency of the clinician sifting through the quantum of data in ABUS volumes to spot lesions. We have implemented a fully automatic generic algorithm pipeline for detection and characterization of lesions on such 3D volumes. We compare a wide range of features for region description on their effectiveness at the dual goals of lesion detection and characterization. On multiple feature images, we compute region descriptors at lesion candidate locations obviating the need for explicit lesion segmentation. We use Random Forests classifier to evaluate candidate region descriptors for lesion detection. Further, we categorize true lesions as Malignant or other masses (e.g. Cysts). Over a database of 145 volumes, with 36 biopsy verified lesions, we achieved Area Under the Curve (AUC) values of 92.6% for lesion detection and 89% for lesion characterization.

Robust PET Motion Correction Using Non-local Spatio-temporal Priors

Respiratory motion presents significant challenges for PET/ CT acquisitions, potentially leading to inaccurate SUV quantitation. Non Rigid Registration [NRR] of gated PET images is quite challenging due to large motion, intrinsic noise, and the need to preserve definitive features like tumors. In this work, we use non-local spatio-temporal constraints within group-wise NRR to get a stable framework which can work with few number of PET gates, and handle the above challenges of PET data. Additionally, we propose metrics for measuring alignment and artifacts introduced by NRR which is rarely addressed. Our results are quantitatively compared to related works, on 20 clinical PET cases.

GPNLPerf: Robust 4d Non-rigid Motion Correction for Myocardial Perfusion Analysis

Since the introduction of wide cone detector systems, CT myocardial perfusion has been an area of increased interest, for which non-rigid registration [NRR] is a key step to further analysis. We propose a novel motion management pipeline for perfusion data, GPNLPerf (Group-wise, non-local, NRR for perfusion analysis) centering on group-wise NRR using non-local spatio-temporal constraints. The proposed pipeline deals with the NRR challenges for 4D perfusion data and results in generating clinically relevant perfusion parameters. We demonstrate results on 9 dynamic perfusion exams comparing results quantitatively with ANTs NRR and also show qualitative results on perfusion maps.