The objectives of the study were to develop a framework for automatic outer and inner breast tissue segmentation using multi-parametric MRI images of the breast tumor patients; and to perform breast density and tumor tissue analysis. MRI of the breast was performed on 30 patients at 3T-MRI. T1, T2 and PD-weighted(W) images, with and without fat saturation(WWFS), and dynamic-contrast-enhanced(DCE)-MRI data were acquired. The proposed automatic segmentation approach was performed in two steps. In step-1, outer segmentation of breast tissue from rest of body parts was performed on structural images (T2-W/T1-W/PD-W without fat saturation images) using automatic landmarks detection technique based on operations like profile screening, Otsu thresholding, morphological operations and empirical observation. In step-2, inner segmentation of breast tissue into fibro-glandular(FG), fatty and tumor tissue was performed. For validation of breast tissue segmentation, manual segmentation was carried out by two radiologists and similarity coefficients(Dice and Jaccard) were computed for outer as well as inner tissues. FG density and tumor volume were also computed and analyzed. The proposed outer and inner segmentation approach worked well for all the subjects and was validated by two radiologists. The average Dice and Jaccard coefficients value for outer segmentation using T2-W images, obtained by two radiologists, were 0.977 and 0.951 respectively. These coefficient values for FG tissue were 0.915 and 0.875 respectively whereas for tumor tissue, values were 0.968 and 0.95 respectively. The volume of segmented tumor ranged over 2.1 cm3–7.08 cm3. The proposed approach provided automatic outer and inner breast tissue segmentation, which enables automatic calculations of breast tissue density and tumor volume. This is a complete framework for outer and inner breast segmentation method for all structural images.
Quantitative parameters, particularly rBBVcorr and K provided similar sensitivity and specificity in differentiating benign from malignant breast lesions for this cohort. Moreover, rBBVcorr provided better differentiation between different grades of malignant breast lesions among all the parameters.
The Electrocardiogram (ECG) signal records the electrical activity of the heart. It is very difficult for physicians to analyze the ECG signal if noise is embedded during acquisition to inspect the heart’s condition. The denoising of electrocardiogram signals based on the genetic particle filter algorithm(GPFA) using fuzzy thresholding and ensemble empirical mode decomposition (EEMD) is proposed in this paper, which efficiently removes noise from the ECG signal. This paper proposes a two-phase scheme for eliminating noise from the ECG signal. In the first phase, the noisy signal is decomposed into a true intrinsic mode function (IMFs) with the help of EEMD. EEMD is better than EMD because it removes the mode-mixing effect. In the second phase, IMFs which are corrupted by noise is obtained by using spectral flatness of each IMF and fuzzy thresholding. The corrupted IMFs are filtered using a GPF method to remove the noise. Then, the signal is reconstructed with the processed IMFs to get the de-noised ECG. The proposed algorithm is analyzed for a different local hospital database, and it gives better root mean square error and signal to noise ratio than other existing techniques (Wavelet transform (WT), EMD, Particle filter(PF) based method, extreme-point symmetric mode decomposition with Nonlocal Means(ESMD-NLM), and discrete wavelet with Savitzky-Golay(DW-SG) filter).
Magnetic resonance imaging (MRI) is playing an important role in the classification of breast tumors. MRI can be used to obtain multiparametric (mp) information, such as structural, hemodynamic, and physiological information. Quantitative analysis of mp‐MRI data has shown potential in improving the accuracy of breast tumor classification. In general, a large set of quantitative and texture features can be generated depending upon the type of methodology used. A suitable combination of selected quantitative and texture features can further improve the accuracy of tumor classification. Machine learning (ML) classifiers based upon features derived from MRI data have shown potential in tumor classification. There is a need for further research studies on selecting an appropriate combination of features and evaluating the performance of different ML classifiers for accurate classification of breast tumors. The objective of the current study was to develop and optimize an ML framework based upon mp‐MRI features for the characterization of breast tumors (malignant vs. benign and low‐ vs. high‐grade). This study included the breast mp‐MRI data of 60 female patients with histopathology results. A total of 128 features were extracted from the mp‐MRI tumor data followed by features selection. Five ML classifiers were evaluated for tumor classification using 10‐fold crossvalidation with 10 repetitions. The support vector machine (SVM) classifier based on optimum features selected using a wrapper method with an adaptive boosting (AdaBoost) technique provided the highest sensitivity (0.96 ± 0.03), specificity (0.92 ± 0.09), and accuracy (94% ± 2.91%) in the classification of malignant versus benign tumors. This method also provided the highest sensitivity (0.94 ± 0.07), specificity (0.80 ± 0.05), and accuracy (90% ± 5.48%) in the classification of low‐ versus high‐grade tumors. These findings suggest that the SVM classifier outperformed other ML methods in the binary classification of breast tumors.
Purpose: This study aimed to evaluate the effect of the registration accuracy of several deformable registration methods on the predictive value of radiomic features to model recurrence-free survival (RFS) in patients undergoing neoadjuvant chemotherapy (NAC) for breast cancer. Methods: From the I-SPY 1 cohort, 130 patients had clinical data and imaging data from the first two visits available, including 38 events (death or recurrence). We computed voxel-wise kinetic maps (peak enhancement, wash-in slope, wash-out slope, and signal enhancement ratio) from both pre-treatment and early-treatment MR images. For each of six different deformable registration methods (ANTs, DRAMMS, ART, NiftyReg, NMI-FFD, and SSD-FFD), we calculated the transformation field and used it to warp the kinetic maps obtained from early treatment MR images. Using these, for each kinetic feature, for each registration method, the parametric response map (PRM) at each voxel computed the difference between the warped kinetic feature and the kinetic feature from the pre-treatment image. We extracted 104 radiomic texture features from each PRM kinetic map, using the CAPTK toolkit, applied principal component (PC) analysis to the 104-dimensional feature vector, and retained the first four PCs for modeling (one covariate for every 10 events). We modeled RFS via Cox proportional hazards, comparing eight models: 1) baseline covariates of age, race, and hormone receptor status (model F1); 2) the covariates in model F1 plus functional tumor volume at the early-treatment visit (FTV2) (model F2); 3-8) the covariates in F2 with the addition of the radiomic feature PCs derived from each registration method. We evaluated model predictive performance using the C-statistic, and model fit via Kaplan-Meier plots and the log-rank test. Results: The baseline model (model F1) provided a C-statistic of 0.54, and model F2 gave 0.66. Among the automated registration methods, the F2+ANTs model had the highest performance with a C-statistic of 0.72. F2+DRAMMS gave 0.70, followed by F2+NiftyReg (0.68), F2+NMI-FFD (0.67), and F2+SSD-FFD (0.67). F2+ART had the lowest performance with a C-statistic of 0.66. The Kaplan-Meier curve for model F2 (baseline + FTV2) gave p = 0.0013 for separation between patients above and below median hazard as compared to the model F1 (p = 0.31). Including FTV2 and radiomic features, all models yielded p < 0.001 except the F2+ART model. Conclusion: The radiomic features of PRM maps derived from warping the DCE-MRI kinetic maps using the ANTs registration method significantly improved early prediction of survival during NAC as compared to other registration methods. Citation Format: Snekha Thakran, Eric Cohen, Nariman Jahani, Susan P. Weinstein, Lauren Pantalone, Nola Hylton, David Newitt, Christos Davatzikos, Despina Kontos. Impact of deformable registration methods for prediction of treatment response to neo-adjuvant chemotherapy in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2804.
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