Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep Learning
Background: Breast cancer is one of the most common malignant tumors in women. A noninvasive ultrasound examination can identify mammary-gland-related diseases and is well tolerated by dense breast, making it a preferred method for breast cancer screening and of significant clinical value. However,...
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| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/10/1220 |
| _version_ | 1797574644789673984 |
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| author | Lei Yang Baichuan Zhang Fei Ren Jianwen Gu Jiao Gao Jihua Wu Dan Li Huaping Jia Guangling Li Jing Zong Jing Zhang Xiaoman Yang Xueyuan Zhang Baolin Du Xiaowen Wang Na Li |
| author_facet | Lei Yang Baichuan Zhang Fei Ren Jianwen Gu Jiao Gao Jihua Wu Dan Li Huaping Jia Guangling Li Jing Zong Jing Zhang Xiaoman Yang Xueyuan Zhang Baolin Du Xiaowen Wang Na Li |
| author_sort | Lei Yang |
| collection | DOAJ |
| description | Background: Breast cancer is one of the most common malignant tumors in women. A noninvasive ultrasound examination can identify mammary-gland-related diseases and is well tolerated by dense breast, making it a preferred method for breast cancer screening and of significant clinical value. However, the diagnosis of breast nodules or masses via ultrasound is performed by a doctor in real time, which is time-consuming and subjective. Junior doctors are prone to missed diagnoses, especially in remote areas or grass-roots hospitals, due to limited medical resources and other factors, which bring great risks to a patient’s health. Therefore, there is an urgent need to develop fast and accurate ultrasound image analysis algorithms to assist diagnoses. Methods: We propose a breast ultrasound image-based assisted-diagnosis method based on convolutional neural networks, which can effectively improve the diagnostic speed and the early screening rate of breast cancer. Our method consists of two stages: tumor recognition and tumor classification. (1) Attention-based semantic segmentation is used to identify the location and size of the tumor; (2) the identified nodules are cropped to construct a training dataset. Then, a convolutional neural network for the diagnosis of benign and malignant breast nodules is trained on this dataset. We collected 2057 images from 1131 patients as the training and validation dataset, and 100 images of the patients with accurate pathological criteria were used as the test dataset. Results: The experimental results based on this dataset show that the MIoU of tumor location recognition is 0.89 and the average accuracy of benign and malignant diagnoses is 97%. The diagnosis performance of the developed diagnostic system is basically consistent with that of senior doctors and is superior to that of junior doctors. In addition, we can provide the doctor with a preliminary diagnosis so that it can be diagnosed quickly. Conclusion: Our proposed method can effectively improve diagnostic speed and the early screening rate of breast cancer. The system provides a valuable aid for the ultrasonic diagnosis of breast cancer. |
| first_indexed | 2024-03-10T21:25:14Z |
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| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-10T21:25:14Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-2ae86f81e1a64e708d7656a366a49c712023-11-19T15:42:42ZengMDPI AGBioengineering2306-53542023-10-011010122010.3390/bioengineering10101220Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep LearningLei Yang0Baichuan Zhang1Fei Ren2Jianwen Gu3Jiao Gao4Jihua Wu5Dan Li6Huaping Jia7Guangling Li8Jing Zong9Jing Zhang10Xiaoman Yang11Xueyuan Zhang12Baolin Du13Xiaowen Wang14Na Li15Strategic Support Force Medical Center, Beijing 100024, ChinaChongqing Zhijian Life Technology Co., Ltd., Chongqing 400039, ChinaState Key Laboratory of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100049, ChinaStrategic Support Force Medical Center, Beijing 100024, ChinaStrategic Support Force Medical Center, Beijing 100024, ChinaStrategic Support Force Medical Center, Beijing 100024, ChinaStrategic Support Force Medical Center, Beijing 100024, ChinaStrategic Support Force Medical Center, Beijing 100024, ChinaCentral Medical District of Chinese PLA General Hospital, Beijing 100080, ChinaStrategic Support Force Medical Center, Beijing 100024, ChinaStrategic Support Force Medical Center, Beijing 100024, ChinaStrategic Support Force Medical Center, Beijing 100024, ChinaChongqing Zhijian Life Technology Co., Ltd., Chongqing 400039, ChinaChongqing Zhijian Life Technology Co., Ltd., Chongqing 400039, ChinaChongqing Zhijian Life Technology Co., Ltd., Chongqing 400039, ChinaChongqing Zhijian Life Technology Co., Ltd., Chongqing 400039, ChinaBackground: Breast cancer is one of the most common malignant tumors in women. A noninvasive ultrasound examination can identify mammary-gland-related diseases and is well tolerated by dense breast, making it a preferred method for breast cancer screening and of significant clinical value. However, the diagnosis of breast nodules or masses via ultrasound is performed by a doctor in real time, which is time-consuming and subjective. Junior doctors are prone to missed diagnoses, especially in remote areas or grass-roots hospitals, due to limited medical resources and other factors, which bring great risks to a patient’s health. Therefore, there is an urgent need to develop fast and accurate ultrasound image analysis algorithms to assist diagnoses. Methods: We propose a breast ultrasound image-based assisted-diagnosis method based on convolutional neural networks, which can effectively improve the diagnostic speed and the early screening rate of breast cancer. Our method consists of two stages: tumor recognition and tumor classification. (1) Attention-based semantic segmentation is used to identify the location and size of the tumor; (2) the identified nodules are cropped to construct a training dataset. Then, a convolutional neural network for the diagnosis of benign and malignant breast nodules is trained on this dataset. We collected 2057 images from 1131 patients as the training and validation dataset, and 100 images of the patients with accurate pathological criteria were used as the test dataset. Results: The experimental results based on this dataset show that the MIoU of tumor location recognition is 0.89 and the average accuracy of benign and malignant diagnoses is 97%. The diagnosis performance of the developed diagnostic system is basically consistent with that of senior doctors and is superior to that of junior doctors. In addition, we can provide the doctor with a preliminary diagnosis so that it can be diagnosed quickly. Conclusion: Our proposed method can effectively improve diagnostic speed and the early screening rate of breast cancer. The system provides a valuable aid for the ultrasonic diagnosis of breast cancer.https://www.mdpi.com/2306-5354/10/10/1220convolutional neural networkultrasoundbreast cancertumor identificationauxiliary diagnosis |
| spellingShingle | Lei Yang Baichuan Zhang Fei Ren Jianwen Gu Jiao Gao Jihua Wu Dan Li Huaping Jia Guangling Li Jing Zong Jing Zhang Xiaoman Yang Xueyuan Zhang Baolin Du Xiaowen Wang Na Li Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep Learning Bioengineering convolutional neural network ultrasound breast cancer tumor identification auxiliary diagnosis |
| title | Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep Learning |
| title_full | Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep Learning |
| title_fullStr | Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep Learning |
| title_full_unstemmed | Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep Learning |
| title_short | Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep Learning |
| title_sort | rapid segmentation and diagnosis of breast tumor ultrasound images at the sonographer level using deep learning |
| topic | convolutional neural network ultrasound breast cancer tumor identification auxiliary diagnosis |
| url | https://www.mdpi.com/2306-5354/10/10/1220 |
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