Deep-learning approach with convolutional neural network for classification of maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging

Fujioka, Tomoyuki; Yashima, Yuka; Oyama, Jun; Mori, M.; Kubota, Kazunori; Katsuta, Leona; Kimura, Koichiro; Yamaga, Emi; Oda, Goshi; Nakagawa, Tsuyoshi; Kitazume, Yoshio; Tateishi, Ukihide

doi:10.1016/j.mri.2020.10.003

Cited by 32 publications

(18 citation statements)

References 22 publications

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“…Under different number of training cycles, the maximum Dice coefficient of CNN-based algorithm reached 0.946, while that of AAM algorithm was 0.843. It indicated that the algorithm of the study had better segmentation effects, which was in line with the research results of Fujioka et al (2021) [ 17 ].…”

Section: Discussionsupporting

confidence: 89%

Convolutional Neural Network-Processed MRI Images in the Diagnosis of Plastic Bronchitis in Children

Chen

Rong

Zhao

2021

Contrast Media & Molecular Imaging

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Objective. The study focused on the features of the convolutional neural networks- (CNN-) processed magnetic resonance imaging (MRI) images for plastic bronchitis (PB) in children. Methods. 30 PB children were selected as subjects, including 19 boys and 11 girls. They all received the MRI examination for the chest. Then, a CNN-based algorithm was constructed and compared with Active Appearance Model (AAM) algorithm for segmentation effects of MRI images in 30 PB children, factoring into occurring simultaneously than (OST), Dice, and Jaccard coefficient. Results. The maximum Dice coefficient of CNN algorithm reached 0.946, while that of active AAM was 0.843, and the Jaccard coefficient of CNN algorithm was also higher (0.894 vs. 0.758, P < 0.05 ). The MRI images showed pulmonary inflammation in all subjects. Of 30 patients, 14 (46.66%) had complicated pulmonary atelectasis, 9 (30%) had the complicated pleural effusion, 3 (10%) had pneumothorax, 2 (6.67%) had complicated mediastinal emphysema, and 2 (6.67%) had complicated pneumopericardium. Also, of 30 patients, 19 (63.33%) had lung consolidation and atelectasis in a single lung lobe and 11 (36.67%) in both two lung lobes. Conclusion. The algorithm based on CNN can significantly improve the segmentation accuracy of MRI images for plastic bronchitis in children. The pleural effusion was a dangerous factor for the occurrence and development of PB.

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Section: Discussionsupporting

confidence: 89%

Convolutional Neural Network-Processed MRI Images in the Diagnosis of Plastic Bronchitis in Children

Chen

Rong

Zhao

2021

Contrast Media & Molecular Imaging

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“…This is called the black box problem. It will be necessary to develop and research deep learning systems that can both provide a diagnosis and clarify the reason for the same [64,65].…”

Section: Discussionmentioning

confidence: 99%

The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review

et al. 2020

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Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women’s health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound.

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“…Research on image classification using the DCNN to solve specific needs has been prominent [13][14][15][16][17][18][19][20][21], as shown in Table 1. In Zhou et al's paper [15], a visual perception technology (VPT) framework based on deep learning was proposed, which relied on the image preprocessing (IP) scheme and the DCNN WR-IPDCNN.…”

Section: Related Workmentioning

confidence: 99%

“…e underlying algorithms in the proposed method were built upon the concept of the DCNN, where the execution time is much less than the time in visual inspection, and the detection and classification process is expected to be significantly less error-prone than that of visual inspection. In Fujioka et al's paper [18], the DCNN was used to distinguish between benign and malignant lesions on the maximum intensity projection of dynamic contrast-enhanced breast magnetic resonance imaging (MRI), and the model showed comparable diagnostic performance. In Alencastre-Miranda et al's paper [19], computer vision and deep learning networks were used to select and plant healthy billets, which increased the plant population and yield per hectare of sugarcane planting.…”

Section: Related Workmentioning

confidence: 99%

Plastic Gasket Defect Detection Based on Transfer Learning

Chen

Wang

Shao

et al. 2021

Scientific Programming

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To automatically detect plastic gasket defects, a set of plastic gasket defect visual detection devices based on GoogLeNet Inception-V2 transfer learning was designed and established in this study. The GoogLeNet Inception-V2 deep convolutional neural network (DCNN) was adopted to extract and classify the defect features of plastic gaskets to solve the problem of their numerous surface defects and difficulty in extracting and classifying the features. Deep learning applications require a large amount of training data to avoid model overfitting, but there are few datasets of plastic gasket defects. To address this issue, data augmentation was applied to our dataset. Finally, the performance of the three convolutional neural networks was comprehensively compared. The results showed that the GoogLeNet Inception-V2 transfer learning model had a better performance in less time. It means it had higher accuracy, reliability, and efficiency on the dataset used in this paper.

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Deep-learning approach with convolutional neural network for classification of maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging

Cited by 32 publications

References 22 publications

Convolutional Neural Network-Processed MRI Images in the Diagnosis of Plastic Bronchitis in Children

Convolutional Neural Network-Processed MRI Images in the Diagnosis of Plastic Bronchitis in Children

The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review

Plastic Gasket Defect Detection Based on Transfer Learning

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