M2UNet:基于UNet的多级特征获取和多输入边缘补充,用于在超声图像中高效地对乳腺瘤进行细分
Lin Pan1, Mengshi Tang1, Xin Chen1
1College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
Diagnostics (Basel, Switzerland)
|May 1, 2025
概括
这项研究引入了一种新的乳腺超声波瘤细分网络. 该方法通过结合多尺度特征和边缘信息来提高细分精度,改善乳腺癌的诊断能力.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 在超声波图像中精确的乳腺瘤细分对于初步癌症诊断至关重要.
- 恶性瘤由于不规则的边缘和不清晰的边界而存在挑战.
- 瘤大小,位置和形状的变化使细分复杂化.
研究的目的:
- 开发一个先进的乳房超声波瘤细分网络.
- 为了提高瘤边界细分的准确性和完整性.
- 通过增强的图像分析,帮助初步诊断乳腺癌.
主要方法:
- 引入了一个使用扩展卷积的多尺度特征采集 (MFA) 模块的网络.
- 实现了多输入边缘补充 (MES) 模块,以增强解码器层输出.
- 专注于捕获各种瘤尺度和精制边界信息.
主要成果:
- 在BUSI和福建癌症医院数据集上实现了高性能指标 (例如,mDice 90.45%,IoU 90.08%在福建数据集上).
- 与BUSI数据集上的UNet相比,大大改善了恶性瘤的Dice得分14.59%,并将Hausdorff距离减少了17.13毫米.
- 证明了精细的细分结果,改善了整体边界完整性.
结论:
- 拟议的方法准确地细分了乳腺瘤超声波图像.
- 该网络提供了宝贵的边缘信息,对于随后的乳腺癌诊断至关重要.
- 实验结果证实了细分精度和诊断支持的实质性进展.
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