Computerized breast lesions detection using kinetic and morphologic analysis for dynamic contrast-enhanced MRI

Chang, Yu‐Cheng; Huang, Yan; Huang, Chiun‐Sheng; Chen, Jeon‐Hor; Chang, Ruey-Feng

doi:10.1016/j.mri.2014.01.008

Cited by 31 publications

(24 citation statements)

References 40 publications

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“…Notice that the contrast agent enhances several structures such as the blood vessels as well as two small lesions that can be appreciated in the center of the left breast. Although, nowadays, this procedure is manually performed, there is an increased interest in computer‐aided diagnosis methods for breast MRI . These algorithms can help radiologists to accelerate the analysis of MR images.…”

Section: Evaluation Methods and Clinical Applicationsmentioning

confidence: 99%

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A step‐by‐step review on patient‐specific biomechanical finite element models for breast MRI to x‐ray mammography registration

et al. 2017

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Breast magnetic resonance imaging (MRI) and x-ray mammography are two image modalities widely used for the early detection and diagnosis of breast diseases in women. The combination of these modalities leads to a more accurate diagnosis and treatment of breast diseases. The aim of this paper is to review the registration between breast MRI and x-ray mammographic images using patient-specific finite element-based biomechanical models. Specifically, a biomechanical model is obtained from the patient's MRI volume and is subsequently used to mimic the mammographic acquisition. Due to the different patient positioning and movement restrictions applied in each image modality, the finite element analysis provides a realistic physics-based approach to perform the breast deformation. In contrast with other reviews, we do not only expose the overall process of compression and registration but we also include main ideas, describe challenges, and provide an overview of the used software in each step of the process. Extracting an accurate description from the MR images and preserving the stability during the finite element analysis require an accurate knowledge about the algorithms used, as well as the software and underlying physics. The wide perspective offered makes the paper suitable not only for expert researchers but also for graduate students and clinicians. We also include several medical applications in the paper, with the aim to fill the gap between the engineering and clinical performance.

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Section: Evaluation Methods and Clinical Applicationsmentioning

confidence: 99%

“…Although, nowadays, this procedure is manually performed, there is an increased interest in computer-aided diagnosis methods for breast MRI. [148][149][150] These algorithms can help radiologists to accelerate the analysis of MR images.…”

Section: Evaluation Methods and Clinical Applicationsmentioning

confidence: 99%

A step‐by‐step review on patient‐specific biomechanical finite element models for breast MRI to x‐ray mammography registration

et al. 2017

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“…Vignati et al [10] and Renz et al [9] do not evaluate the performance of their methods on non-mass-like enhancement lesions. Chang et al [11] and Ertas et al [8] do not clearly express whether their evaluations are performed on non-mass-like lesions.…”

Section: False Positive Reductionmentioning

confidence: 99%

“…However, the mentioned dataset does not include benign lesions while the discrimination of benign lesions from the normal breast tissues is one of the challenging issues in a CAD system. Among the mentioned studies, only GubernMérida et al [14] and Chang et al [11] present their results based on FROC analysis. Vignati et al [10] and Renz et al [9] do not evaluate the performance of their methods on non-mass-like enhancement lesions.…”

Section: False Positive Reductionmentioning

confidence: 99%

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Computer-aided detection of breast lesions in DCE-MRI using region growing based on fuzzy C-means clustering and vesselness filter

Shokouhi

Fooladivanda

Ahmadinejad

2017

EURASIP J. Adv. Signal Process.

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A computer-aided detection (CAD) system is introduced in this paper for detection of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The proposed CAD system firstly compensates motion artifacts and segments the breast region. Then, the potential lesion voxels are detected and used as the initial seed points for the seeded region-growing algorithm. A new and robust region-growing algorithm incorporating with Fuzzy C-means (FCM) clustering and vesselness filter is proposed to segment any potential lesion regions. Subsequently, the false positive detections are reduced by applying a discrimination step. This is based on 3D morphological characteristics of the potential lesion regions and kinetic features which are fed to the support vector machine (SVM) classifier. The performance of the proposed CAD system is evaluated using the free-response operating characteristic (FROC) curve. We introduce our collected dataset that includes 76 DCE-MRI studies, 63 malignant and 107 benign lesions. The prepared dataset has been used to verify the accuracy of the proposed CAD system. At 5.29 false positives per case, the CAD system accurately detects 94% of the breast lesions.

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Artificial intelligence in the interpretation of breast cancer on MRI

Sheth

Giger

2019

Magnetic Resonance Imaging

148

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Advances in both imaging and computers have led to the rise in the potential use of artificial intelligence (AI) in various tasks in breast imaging, going beyond the current use in computer-aided detection to include diagnosis, prognosis, response to therapy, and risk assessment. The automated capabilities of AI offer the potential to enhance the diagnostic expertise of clinicians, including accurate demarcation of tumor volume, extraction of characteristic cancer phenotypes, translation of tumoral phenotype features to clinical genotype implications, and risk prediction. The combination of image-specific findings with the underlying genomic, pathologic, and clinical features is becoming of increasing value in breast cancer. The concurrent emergence of newer imaging techniques has provided radiologists with greater diagnostic tools and image datasets to analyze and interpret. Integrating an AI-based workflow within breast imaging enables the integration of multiple data streams into powerful multidisciplinary applications that may lead the path to personalized patient-specific medicine. In this article we describe the goals of AI in breast cancer imaging, in particular MRI, and review the literature as it relates to the current application, potential, and limitations in breast cancer. Level of Evidence: 3 Technical Efficacy: Stage 3

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Computerized breast lesions detection using kinetic and morphologic analysis for dynamic contrast-enhanced MRI

Cited by 31 publications

References 40 publications

A step‐by‐step review on patient‐specific biomechanical finite element models for breast MRI to x‐ray mammography registration

A step‐by‐step review on patient‐specific biomechanical finite element models for breast MRI to x‐ray mammography registration

Computer-aided detection of breast lesions in DCE-MRI using region growing based on fuzzy C-means clustering and vesselness filter

Artificial intelligence in the interpretation of breast cancer on MRI

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