Dynamic MR imaging of the breast: Analysis of kinetic and morphologic diagnostic criteria

Szabó, Botond K.; Aspelin, Peter; Wiberg, Maria Kristoffersen; Boné, B.

doi:10.1080/j.1600-0455.2003.00084.x

Cited by 53 publications

(47 citation statements)

References 28 publications

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“…While only dynamic signal intensity characteristics are integrated in today's CAD systems leaving morphological features to the interpretation of the radiologist, an automated diagnosis based on a combination of both should be strived for. Clinical studies have shown that combinations of different dynamic and morphologic characteristics [2] achieve diagnostic sensitivities up to 97% and specificities up to 76.5%.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Nonrigid Motion Compensation Technique Based on Spatiotemporal Features for Small Lesion Detection in Breast MRI

Steinbruecker

Meyer‐Baese

Schlossbauer³

et al. 2012

Advances in Artificial Neural Systems

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Motion-induced artifacts represent a major problem in detection and diagnosis of breast cancer in dynamic contrast-enhanced magnetic resonance imaging. The goal of this paper is to evaluate the performance of a new nonrigid motion correction algorithm based on the optical flow method. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set under consideration of several 2D or 3D motion compensation parameters for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal motion compensation parameters. Our results have shown that motion compensation can improve the classification results. The results suggest that the computerized analysis system based on the non-rigid motion compensation technique and spatiotemporal features has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography.

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Section: Introductionmentioning

confidence: 99%

Evaluation of a Nonrigid Motion Compensation Technique Based on Spatiotemporal Features for Small Lesion Detection in Breast MRI

Steinbruecker

Meyer‐Baese

Schlossbauer³

et al. 2012

Advances in Artificial Neural Systems

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“…E 4 attempts to find the tumor boundary in the vicinity of manually placed contour, and is defined for the pixel pairs which are neighboring and belong differently to tumor and background as E 4 …”

Section: Segmentationmentioning

confidence: 99%

“…The information in such dynamic and architectural features is, however, limited in the scope in the sense that their combination lacks comprehensive spatial variations of pixel-wise temporal enhancements (TE). These spatial patterns have been shown to be fundamentally important for distinguishing malignant and benign tumors [4,1]. Although the spatial information incorporated in [4,1] offers some improvement, their dependence on qualitative ratings by experts limits their utility for automatic diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…High sensitivity of DCE-MRI to breast cancer detection is, however, confounded by its relatively low specificity [2]. Existing approaches attempt to improve low specificity through better segmentation [3] and/or complete characterization of tumor (using architectural and dynamic features [4,1]). …”

Section: Introductionmentioning

confidence: 99%

“…Two such features, namely dynamic and architectural features [4,1,2,3], have extensively been investigated for quantifying tumor properties. For instance, an early strong enhancement with rapid washout has been observed in malignant tumors.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation and Classification of Breast Tumor Using Dynamic Contrast-Enhanced MR Images

Zheng

Baloch

Englander

et al.

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007

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Abstract. Accuracy of automatic cancer diagnosis is largely determined by two factors, namely, the precision of tumor segmentation, and the suitability of extracted features for discrimination between malignancy and benignancy. In this paper, we propose a new framework for accurate characterization of tumors in contrast enhanced MR images. First, a new graph cut based segmentation algorithm is developed for refining coarse manual segmentation, which allows precise identification of tumor regions. Second, by considering serial contrast-enhanced images as a single spatio-temporal image, a spatio-temporal model of segmented tumor is constructed to extract Spatio-Temporal Enhancement Patterns (STEPs). STEPs are designed to capture not only dynamic enhancement and architectural features, but also spatial variations of pixel-wise temporal enhancement of the tumor. While temporal enhancement features are extracted through Fourier transform, the resulting STEP framework captures spatial patterns of temporal enhancement features via moment invariants and rotation invariant Gabor textures. High accuracy of the proposed framework is a direct consequence of this two pronged approach, which is validated through experiments yielding, for instance, an area of 0.97 under the ROC curve.

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AI‐Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer

Meyer-Bäse

Morra

Tahmassebi

et al. 2020

Magnetic Resonance Imaging

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Computer‐aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a “second opinion” review complementing the radiologist's review. CAD systems have many common parts, such as image preprocessing, tumor feature extraction, and data classification that are mostly based on machine‐learning (ML) techniques. In this review article, we describe applications of ML‐based CAD systems in MRI covering the detection of diagnostically challenging lesions of the breast such as nonmass enhancing (NME) lesions, and furthermore discuss how multiparametric MRI and radiomics can be applied to the study of NME, including prediction of response to neoadjuvant chemotherapy (NAC). Since ML has been widely used in the medical imaging community, we provide an overview about the state‐of‐the‐art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples, illustrating: 1) CAD for detection and diagnosis, 2) CAD in multiparametric imaging, 3) CAD in NAC, and 4) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on machine and deep learning in MRI of the breast. Level of Evidence 2 Technical Efficacy Stage 2

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Dynamic MR imaging of the breast: Analysis of kinetic and morphologic diagnostic criteria

Abstract: Time-to-peak enhancement and the descriptor of margins appear to be the most important diagnostic criteria for mass lesions in dynamic breast MR imaging.

Cited by 53 publications

References 28 publications

Evaluation of a Nonrigid Motion Compensation Technique Based on Spatiotemporal Features for Small Lesion Detection in Breast MRI

Evaluation of a Nonrigid Motion Compensation Technique Based on Spatiotemporal Features for Small Lesion Detection in Breast MRI

Segmentation and Classification of Breast Tumor Using Dynamic Contrast-Enhanced MR Images

AI‐Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer

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