Conference Paper

Concept detection in longitudinal brain MR images using multi-modal cues

Univ. of Maryland, Baltimore County, Catonsville, MD, USA
DOI: 10.1109/ISBI.2009.5193073 Conference: Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on
Source: IEEE Xplore

ABSTRACT

Advances in medical imaging techniques and devices has resulted in increased use of imaging in monitoring disease progression in patients. However, extracting decision-enabling information from the resulting longitudinal multi-modal image sets poses a challenge. Radiologists often have to manually identify and quantify certain regions of interest in the longitudinal image sets, which bear upon the patient's condition. As the number of patients increases, the number of longitudinal multi-modal images grows, and the manual annotation and quantification of pathological concepts quickly becomes impractical. In this paper we explore how minimal annotations provided by the user at a few time points can be effectively leveraged to automatically annotate data in the entire multi-modal longitudinal image sets. In particular, we investigate the required number of annotated images per time point and across time for obtaining reasonable results for the entire image set, and what multi-modal cues can help boost the overall annotation results.

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    • "Since the seed point for tracking is random innature, this technique is not much efficient. Acontrast agent accumulation model based contrastenhancement is implemented by [19]. "

    Preview · Article · May 2011
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    • "tracking, detection of edema progression [3], and temporal tumor segmentation [2]. In [3] the authors presented research on edema detection in longitudinal multi-modal brain magnetic resonance images with minimal expert intervention. By combining transductive and inductive machine learning methods the approach allowed to automatically extract regions of phenotypic disorders. "
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    ABSTRACT: In this review, we will focus on advances in several imaging modalities, and their relationships to specific disease states. In the first section we discuss the role of 4-D ultrasound in estimating cardiac function, and how such information may be combined with additional data for assessment of ischemia. Section II describes the role of multimodal imaging and segmentation in computer-aided treatment planning for liver cancer. In Section III, we explore new methodologies in the fusion of PET data with complementary images from MRI and CT, with applications in the leading types of cancer. Finally, in Section IV, recent advances in measuring cerebral blood volume are described.
    Preview · Article · Jan 2011
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    • "tracking, detection of edema progression [3], and temporal tumor segmentation [2]. In [3] the authors presented research on edema detection in longitudinal multi-modal brain magnetic resonance images with minimal expert intervention. By combining transductive and inductive machine learning methods the approach allowed to automatically extract regions of phenotypic disorders. "
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    ABSTRACT: This article resumes on a selected set of topics and collects promising and recent research advances in the field of multimodal temporal data analysis, high-field magnetic resonance spectroscopy, trends in computer-aided diagnosis and advances in cardiac diagnostic imaging. The first section briefly points to promising work on statistical models for tracking, detection, and segmentation in multimodal temporal imagery. Section III gives a brief snapshot of slice selective free induction decay (FID) acquisition for 7 tesla high-field MR imaging. Section IV outlines highlights in comparative validation of computer-aided diagnosis and associated image analysis algorithms spanning a variety of application domains from the heart to the eye. Lastly, Section V describes advances in the analysis of real-time three-dimensional (3-D) echocardiography for computing myocardial strain.
    Preview · Article · Feb 2009 · IEEE Reviews in Biomedical Engineering
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