Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non–Fat-Sat Images and Tested on Fat-Sat Images

Zhang, Yang; Chan, Si-Wa; Park, Vivian Youngjean; Chang, Kai Ting; Mehta, Siddharth; Kim, Min Jung; Combs, Freddie J.; Chang, Peter D.; Chow, Daniel; Parajuli, Ritesh; Mehta, Rita S.; Lin, Chin Yao; Chien, Sou Hsin; Chen, Jeon Hor; Su, Min‐Ying

doi:10.1016/j.acra.2020.12.001

Cited by 47 publications

(37 citation statements)

References 36 publications

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“…The results of the above comparison further verified the superiority of Mask R-CNN algorithm in image segmentation. Zhang et al [ 26 ] adopted the deep learning of Mask R-CNN algorithm to offer a new method to the location and segmentation of MRI of breast lesions. Toufani et al [ 27 ] utilized Mask R-CNN algorithm to segment the spinal cord cross-sectional area (SCCSA) of each section.…”

Section: Discussionmentioning

confidence: 99%

Analgesic Effects of Dexmedetomidine Combined with Spinal and Epidural Anesthesia Nursing on Prostate Hyperplasia Patients after Transurethral Resection of Prostate by Intelligent Algorithm-Based Magnetic Resonance Imaging

Zhang

Zeng

et al. 2022

Computational and Mathematical Methods in Medicine

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To analyze the investigation of the application effects of different doses of dexmedetomidine (Dex) with combined spinal and epidural anesthesia nursing on analgesia after transurethral resection of prostate (TURP) by intelligent algorithm-based magnetic resonance imaging (MRI), MRI imaging segmentation model of mask regions with convolutional neural network (Mask R-CNN) features was proposed in the research. Besides, the segmentation effects of Mask R-CNN, U-net, and V-net algorithms were compared and analyzed. Meanwhile, a total of 184 patients receiving TURP were selected as the research objects, and they were divided into A, B, C, and D groups based on random number table method, each group including 46 cases. Patients in each group were offered different doses of Dex, and visual analogue scale (VAS) and Ramsay scores of different follow-up visit time, use of other analgesics, the incidence of postoperative cystospasm, and nursing satisfaction of patients in four groups were compared. The results demonstrated that Dice similarity coefficient (DSC) value, specificity, and positive predictive value of Mask R-CNN algorithm were 0.623 ± 0.084 , 98.61%, and 69.57%, respectively, all of which were higher than those of U-net and V-net algorithms. Pain VAS scores and the incidence of cystospasm at different time periods of groups B and C were both significantly lower than those of group D ( P < 0.05 ). Ramsay scores of groups B and C at 8 hours, 12 hours, 24 hours, and 48 hours after the operation were all remarkably higher than those in group D ( P < 0.05 ). Besides, nursing satisfaction of groups B and C was obviously superior to that in group D, and the difference demonstrated statistical meaning ( P < 0.05 ). The differences revealed that Dex showed excellent analgesic and sedative effects and could effectively reduce the incidence of complications after TURP, including cystospasm and nausea. In addition, it helped improve nursing satisfaction and patient prognosis.

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Section: Discussionmentioning

confidence: 99%

Analgesic Effects of Dexmedetomidine Combined with Spinal and Epidural Anesthesia Nursing on Prostate Hyperplasia Patients after Transurethral Resection of Prostate by Intelligent Algorithm-Based Magnetic Resonance Imaging

Zhang

Zeng

et al. 2022

Computational and Mathematical Methods in Medicine

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“…In Zhang et al (27), only 1 time-point of the DCE-MRI series was chosen. The strongest enhancement phase can better reflect the tumor heterogeneity and invasiveness by relying upon the subtracted DCE-MRI images according to the literature (27,28). Therefore, our choice to analyze a single phase is consistent with the literature.…”

Section: Lesion Segmentationmentioning

confidence: 89%

3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients

et al. 2022

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Rationale and Objectives: To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radiomics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation).Materials and Methods: 107 radiomic features were extracted from a manually annotated dataset of 111 patients, which was split into discovery and test sets. A feature calibration and pre-processing step was performed to find only robust non-redundant features. An indepth discovery analysis was performed to define a predictive model: for this purpose, a Support Vector Machine (SVM) was trained in a nested 5-fold cross-validation scheme, by exploiting several unsupervised feature selection methods. The predictive model performance was evaluated in terms of Area Under the Receiver Operating Characteristic (AUROC), specificity, sensitivity, PPV and NPV. The test was performed on unseen held-out data.Results: The model combining Unsupervised Discriminative Feature Selection (UDFS) and SVMs on average achieved the best performance on the blinded test set: AUROC = 0.725 §0.091, sensitivity = 0.709 §0.176, specificity = 0.741 §0.114, PPV = 0.72 §0.093, and NPV = 0.75 §0.114. Conclusion:In this study, we built a radiomic predictive model based on breast DCE-MRI, using only the strongest enhancement phase, with promising results in terms of accuracy and specificity in the differentiation of malignant from benign breast lesions.

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“…Model 3 is deeper than Models 1 and 2, with a five-layer CNN used to detect breast cancer [37]. is architecture gives the best result with 87% accuracy as shown in Table 6: it also provides a similar distribution of predicted labels to that of actual labels (50/50).…”

Section: Predicting Invasive Cancer Using Cnn Model 3 Cnnmentioning

confidence: 99%

Boosting Breast Cancer Detection Using Convolutional Neural Network

Alanazi

Kamruzzaman

Sarker

et al. 2021

Journal of Healthcare Engineering

146

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Breast cancer forms in breast cells and is considered as a very common type of cancer in women. Breast cancer is also a very life-threatening disease of women after lung cancer. A convolutional neural network (CNN) method is proposed in this study to boost the automatic identification of breast cancer by analyzing hostile ductal carcinoma tissue zones in whole-slide images (WSIs). The paper investigates the proposed system that uses various convolutional neural network (CNN) architectures to automatically detect breast cancer, comparing the results with those from machine learning (ML) algorithms. All architectures were guided by a big dataset of about 275,000, 50 × 50-pixel RGB image patches. Validation tests were done for quantitative results using the performance measures for every methodology. The proposed system is found to be successful, achieving results with 87% accuracy, which could reduce human mistakes in the diagnosis process. Moreover, our proposed system achieves accuracy higher than the 78% accuracy of machine learning (ML) algorithms. The proposed system therefore improves accuracy by 9% above results from machine learning (ML) algorithms.

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Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non–Fat-Sat Images and Tested on Fat-Sat Images

Cited by 47 publications

References 36 publications

Analgesic Effects of Dexmedetomidine Combined with Spinal and Epidural Anesthesia Nursing on Prostate Hyperplasia Patients after Transurethral Resection of Prostate by Intelligent Algorithm-Based Magnetic Resonance Imaging

Analgesic Effects of Dexmedetomidine Combined with Spinal and Epidural Anesthesia Nursing on Prostate Hyperplasia Patients after Transurethral Resection of Prostate by Intelligent Algorithm-Based Magnetic Resonance Imaging

3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients

Boosting Breast Cancer Detection Using Convolutional Neural Network

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