Automated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaging (original) (raw)
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Automated non-mass enhancing lesion detection and segmentation in breast DCE-MRI
arXiv (Cornell University), 2018
Non-mass enhancing lesions (NME) constitute a diagnostic challenge in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer Aided Diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment and evaluation that have a significant impact on the diagnostic performance. Here, we propose a new approach to address the challenge of NME detection and segmentation, taking advantage of independent component analysis (ICA) to extract data-driven dynamic lesion characterizations. A set of independent sources was obtained from DCE-MRI dataset of breast patients, and the dynamic behavior of the different tissues was described by multiple dynamic curves, together with a set of eigenimages describing the scores for each voxel. A new test image is projected onto the independent source space using the unmixing matrix, and each voxel is classified by a support vector machine (SVM) that has already been trained with manually delineated data. A solution to the high false positive rate problem is proposed by controlling the SVM hyperplane location, outperforming previously published approaches.
Segmentation and Kinetic Analysis of Breast Lesions in DCE-MR Imaging Using ICA
Lecture Notes in Computer Science, 2014
Dynamic Contrast Enhance-Magnetic Resonance Imaging (DCE-MRI) has proved to be a useful tool for diagnosing mass-like breast cancer. For non-mass-like lesions, however, no methods applied on DCE-MRI have shown satisfying results so far. The present paper uses the Independent Component Analysis (ICA) to extract tumor enhancement curves which are more exact than manually or automatically chosen regions of interest (ROIs). By analysing the different tissue types contained in the voxels of the MR image, we can filter out noise and define lesion related enhancement curves. These curves allow a better classification than ROI or segmentation methods. This is illustrated by extracting features from MRI cases and determining the malignancy or benignity by support vector machines (SVMs). Next to this classification by kinetic analysis, ICA is also used to segment tumorous regions. Unlike in standard segmentation methods, we do not regard voxels as a whole but instead focus our analysis on the actual tissue types, and filter out noise. Combining all these achievements we present a complete workflow for classification of malignant and benign lesions providing helpful support for the fight against breast cancer.
Proceedings / ICIP ... International Conference on Image Processing, 2008
Computer-aided diagnosis and simultaneous visualization based on independent component analysis and clustering are integrated in an intelligent system for the evaluation of small mammographic lesions in breast MRI. These techniques are tested on biomedical time-series representing breast MRI scans and enable the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By revealing regional properties of contrast-agent uptake characterized by subtle differences of signal amplitude and dynamics, these methods provide both a set of prototypical time-series and a corresponding set of cluster assignment maps which further provide a segmentation with regard to identification and regional subclassification of pathological breast tissue lesions. Both approaches lead to an increase of the diagnostic accuracy of MRI mammography by improving the sensitivity without reduction of specificity.
IEEE Transactions on Medical Imaging, 2008
Early detection of breast cancer is one of the most important factors in determining prognosis for women with malignant tumours. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been shown to be the most sensitive modality for screening high-risk women. Computer-aided diagnosis (CAD) systems have the potential to assist radiologists in the early detection of cancer. A key component of the development of such a CAD system will be the selection of an appropriate classification function responsible for separating malignant and benign lesions. The purpose of this study is to evaluate the effects of variations in temporal feature vectors and kernel functions on the separation of malignant and benign DCE-MRI breast lesions by support vector machines. We also propose and demonstrate a classifier visualization and evaluation technique. We show that support vector machines provide an effective and flexible framework from which to base computer-aided diagnosis techniques for breast MR imaging, and that the proposed classifier visualization technique has potential as a mechanism for the evaluation of classification solutions.
Engineering Applications of Artificial Intelligence, 2008
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important tool in breast cancer diagnosis, but evaluation of multitemporal 3D image data holds new challenges for human observers. To aid the image analysis process, we apply supervised and unsupervised pattern recognition techniques for computing enhanced visualizations of suspicious lesions in breast MRI data. These techniques represent an important component of future sophisticated computer-aided diagnosis (CAD) systems and support the visual exploration of spatial and temporal features of DCE-MRI data stemming from patients with confirmed lesion diagnosis. By taking into account the heterogeneity of cancerous tissue, these techniques reveal signals with malignant, benign and normal kinetics. They also provide a regional subclassification of pathological breast tissue, which is the basis for pseudo-color presentations of the image data. Intelligent medical systems are expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging.
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014
Diagnosis of breast nonmass lesions, most notably ductal carcinoma in situ, is challenging. Recent studies show that dynamic contrast enhanced MRI achieves high sensitivity in diagnosis of nonmass lesions. Unlike successfully applied to diagnose mass lesions, particularly kinetic features are reported to be less effective in discriminating nonmass lesions. It is even difficult for human observers to differentiate nonmass lesions against the enhancing parenchymal or benign lesions due to their sometimes similar morphology and contrast kinetics. Towards an automated computer-aided diagnosis system of nonmass lesions, we implemented an extendable and completely automated framework that is efficient and modularized, aiming to discriminate detected suspicious regions into malignant and benign. The entire framework consists of five sequentially executed modules: motion correction, segmentation of breast regions, detection of suspicious regions, feature extraction, and knowledge-based analysis of suspicious regions. A preliminary test was performed on a data set collecting 162 nonmass lesions extracted from 67 patients, which achieved an area under ROC curve value of 0.74 for malignant lesions.
International journal of biomedical imaging, 2009
An application of an unsupervised neural network-based computer-aided diagnosis (CAD) system is reported for the detection and characterization of small indeterminate breast lesions, average size 1.1 mm, in dynamic contrast-enhanced MRI. This system enables the extraction of spatial and temporal features of dynamic MRI data and additionally provides a segmentation with regard to identification and regional subclassification of pathological breast tissue lesions. Lesions with an initial contrast enhancement >/=50% were selected with semiautomatic segmentation. This conventional segmentation analysis is based on the mean initial signal increase and postinitial course of all voxels included in the lesion. In this paper, we compare the conventional segmentation analysis with unsupervised classification for the evaluation of signal intensity time courses for the differential diagnosis of enhancing lesions in breast MRI. The results suggest that the computerized analysis system based o...
Medical physics, 2012
Purpose: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a radiological tool for the detection and discrimination of breast lesions. The aim of this study is to evaluate a computer-aided diagnosis (CAD) system for discriminating malignant from benign breast lesions at DCE-MRI by the combined use of morphological, kinetic, and spatiotemporal lesion features.Methods: Fifty-four malignant and 19 benign breast lesions in 51 patients were retrospectively evaluated. Images were acquired at two centers at 1.5 T. Mass-like lesions were automatically segmented after image normalization and elastic coregistration of contrast-enhanced frames. For each lesion, a set of 28 3D features were extracted: ten morphological (related to shape, margins, and internal enhancement distribution); nine kinetic (computed from signal-to-time curves); and nine spatiotemporal (related to the variation of the signal between adjacent frames). A support vector machine (SVM) was trained with featur...
Artificial Intelligence in Medicine, 2013
Objective: The early detection of breast cancer is one of the most important predictors in determining the prognosis for women with malignant tumours. Dynamic contrast-enhanced magnetic-resonance imaging (DCE-MRI) is an important imaging modality for detecting and interpreting the different breast lesions from a time sequence of images and has proved to be a very sensitive modality for breast-cancer diagnosis. However, DCE-MRI exhibits only a moderate specificity, thus leading to a high rate of false positives, resulting in unnecessary biopsies that are stressful and physically painful for the patient and lead to an increase in the cost of treatment. There is a strong medical need for a DCE-MRI computeraided diagnosis tool that would offer a reliable support to the physician's decision providing a high level of sensitivity and specificity. Methods: In our study we investigated the possibility of increasing differentiation between the malignant and the benign lesions with respect to the spatial variation of the temporal enhancements of three parametric maps, i.e., the initial enhancement (IE) map, the post-initial enhancement (PIE) map and the signal enhancement ratio (SER) map, by introducing additional methods along with the grey-level co-occurrence matrix, i.e., a second-order statistical method already applied for quantifying the spatiotemporal variations. We introduced the grey-level run-length matrix and the grey-level difference matrix, representing two additional, second-order statistical methods, and the circular Gabor as a frequency-domain-based method. Each of the additional methods is for the first time applied to the DCE-MRI data to differentiate between the malignant and the benign breast lesions. We applied the least-square minimum-distance classifier (LSMD), logistic regression and least-squares support vector machine (LS-SVM) classifiers on a total of 115 (78 malignant and 37 benign) breast DCE-MRI cases. The performances were evaluated using ten experiments of a tenfold cross-validation. Results: Our experimental analysis revealed the PIE map, together with the feature subset in which the discriminating ability of the co-occurrence features was increased by adding the newly introduced features, to be the most significant for differentiation between the malignant and the benign lesions. That diagnostic test-the aforementioned combination of parametric map and the feature subset achieved the sensitivity of 0.9193 which is statistically significantly higher compared to other diagnostic tests after ten-experiments of a tenfold cross-validation and gave a statistically significantly higher specificity of 0.7819 for the fixed 95% sensitivity after the receiver operating characteristic (ROC) curve analysis. Combining the information from all the three parametric maps significantly increased the area under the ROC curve (AUC) of the aforementioned diagnostic test for the LSMD and logistic regression; however, not for the LS-SVM. The LSMD classifier yielded the highest area under the ROC curve when using the combined information, increasing the AUC from 0.9651 to 0.9755. Conclusion: Introducing new features to those of the grey-level co-occurrence matrix significantly increased the differentiation between the malignant and the benign breast lesions, thus resulting in a high sensitivity and improved specificity.
Detection of suspicious lesions in dynamic contrast enhanced MRI data
The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important source of information to aid breast cancer diagnosis. Nevertheless, next to the temporal sequence of 3D volume data from the DCE-MRI technique, the radiologist commonly adducts information from other modalities for his final diagnosis. Thus, the diagnosis process is time consuming and tools are required to support the human expert. We investigate an automatic approach that detects the location and delineates the extent of suspicious masses in multi-temporal DCE-MRI data sets. It applies the state-of-the-art support vector machine algorithm to the classification of the short-time series associated with each voxel. The ROC analysis shows an increased specificity in contrast to standard evaluations techniques.