Dr. Lina Arbash Meinel Profile

Dr. Lina Arbash Meinel

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Publications (4)

Proceedings Article | 9 March 2010 Paper

Heterogeneity of kinetic curve parameters as indicator for the malignancy of breast lesions in DCE MRI

Hans Buurman, Lina Arbash Meinel, Martin Bergtholdt, Rafael Wiemker, Gillian Newstead, Thomas Buelow, Axel Saalbach

Proceedings Volume 7624, 76241H (2010) https://doi.org/10.1117/12.844365

KEYWORDS: Image segmentation, Breast, Magnetic resonance imaging, Tumors, Medical research, Scanners, Pathology, Diagnostics, Breast cancer, Tissues

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Proceedings Article | 4 March 2009 Paper

Multi-modality computer-aided diagnosis system for axillary lymph node (ALN) staging: segmentation of ALN on ultrasound images

Gillian Newstead, Martin Bergtholdt, Lina Arbash Meinel, D. Huo, Thomas Buelow, Ingwer Carlsen, Hiroyuki Abe

Proceedings Volume 7260, 72602D (2009) https://doi.org/10.1117/12.811212

KEYWORDS: Aluminum nitride, Image segmentation, Lymphatic system, Ultrasonography, Computer aided diagnosis and therapy, Computing systems, Breast cancer, Magnetic resonance imaging, Francium, Image processing algorithms and systems

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Proceedings Article | 3 March 2009 Paper

Temporal variations in apparent breast lesion volume in dynamic contrast enhanced breast MR imaging

Hans Buurman, Lina Arbash Meinel, Thomas Buelow, Rafael Wiemker, Ursula Kose, Gillian Newstead

Proceedings Volume 7260, 726029 (2009) https://doi.org/10.1117/12.812220

KEYWORDS: Image segmentation, Breast, Magnetic resonance imaging, Image processing algorithms and systems, Tumors, Mammography, Breast cancer, Cancer, Diagnostics, Algorithm development

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Dynamic Contrast Enhanced Breast MR Imaging (DCE BMRI) has emerged as a modality for breast cancer diagnosis. In this modality a temporal sequence of volume images of the breasts is acquired, where a contrast agent is injected after acquisition of the first 3D image. Since the introduction of the modality, research has been directed at the development of computer-aided support for the diagnostic workup. This includes automatic segmentation of mass-like lesions, lesion characterization, and lesion classification. Robustness, user-independence, and reproducibility of the results of computerized methods are essential for such methods to be acceptable for clinical application. A previously proposed and evaluated computerized lesion segmentation method has been further analyzed in this study. The segmentation method uses as input a subtraction image (post-contrast - pre-contrast) and a user defined region of interest (ROI). Previous evaluation studies investigated the robustness of the segmentation against variations in the user selected ROI. Robustness of the method against variations in the image data itself has so far not been investigated. To fill this gap is the purpose of this study. In this study, the segmentation algorithm was applied to a series of subtraction images built from the pre-contrast volume and all available post-contrast image volumes, successively. This provides set of typically 4-5 delineations per lesion, each based on a different phase of the dynamic sequence. Analysis of the apparent lesion volumes derived from these delineations and comparison to manual delineations showed that computerized segmentation is more robust and reproducible than manual segmentation, even if computer segmentations are computed on subtraction images derived from different dynamic phases of the DCE MRI study, while all manual segmentations of a lesion are derived from one and the same dynamic phase of the study. Furthermore, it could be shown that the rate of apparent change of lesion volume over the course of a DCE MRI study is significantly dependent on the lesion type (benign vs. malignant).

Proceedings Article | 29 March 2007 Paper

Segmentation of suspicious lesions in dynamic contrast-enhanced breast MR images

Lina Arbash Meinel, Akiko Shimauchi, Rafael Wiemker, Ursula Kose, Gillian Newstead, Thomas Bülow

Proceedings Volume 6514, 65140T (2007) https://doi.org/10.1117/12.706537

KEYWORDS: Image segmentation, Breast, Magnetic resonance imaging, Computer aided diagnosis and therapy, Breast cancer, Tumors, Feature extraction, Optical spheres, Diagnostics, Data acquisition

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