Ding Ting, Shi Kaimai, Pan Zhaoyan, Ding Cheng
School of Earth Science, East China University of Technology, Nanhang, JiangXi, China.
Urumqi Comprehensive Survey Center on Natural Resources, Urumq, XinJiang, China.
PeerJ Comput Sci. 2024 Oct 11;10:e2226. doi: 10.7717/peerj-cs.2226. eCollection 2024.
Breast cancer is a leading cause of death among women worldwide, making early detection and diagnosis critical for effective treatment and improved patient outcomes. Ultrasound imaging is a common diagnostic tool for breast cancer, but interpreting ultrasound images can be challenging due to the complexity of breast tissue and the variability of image quality. This study proposed an Attention Gated Multi ResU-Net model for medical image segmentation tasks, that has shown promising results for breast cancer ultrasound image segmentation. The model's multi-scale feature extraction and attention-gating mechanism enable it to accurately identify and segment areas of abnormality in the breast tissue, such as masses, cysts, and calcifications. The model's quantitative test showed an adequate degree of agreement with expert manual annotations, demonstrating its potential for improving early identification and diagnosis of breast cancer. The model's multi-scale feature extraction and attention-gating mechanism enable it to accurately identify and segment areas of abnormality in the breast tissue, such as masses, cysts, and calcifications, achieving a Dice coefficient of 0.93, sensitivity of 93%, and specificity of 99%. These results underscore the model's high precision and reliability in medical image analysis.
乳腺癌是全球女性死亡的主要原因之一，因此早期检测和诊断对于有效治疗和改善患者预后至关重要。超声成像是乳腺癌的一种常见诊断工具，但由于乳腺组织的复杂性和图像质量的变异性，解读超声图像可能具有挑战性。本研究提出了一种用于医学图像分割任务的注意力门控多ResU-Net模型，该模型在乳腺癌超声图像分割方面已显示出有前景的结果。该模型的多尺度特征提取和注意力门控机制使其能够准确识别和分割乳腺组织中的异常区域，如肿块、囊肿和钙化。该模型的定量测试显示与专家手动标注有足够程度的一致性，证明了其在改善乳腺癌早期识别和诊断方面的潜力。该模型的多尺度特征提取和注意力门控机制使其能够准确识别和分割乳腺组织中的异常区域，如肿块、囊肿和钙化，达到了0.93的Dice系数、93%的灵敏度和99%的特异性。这些结果强调了该模型在医学图像分析中的高精度和可靠性。
