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边界意识的语义聚类网络用于从T2加权的MRI中对前列腺区域进行细分

Weixuan Kou¹, Harry Marshall², Bernard Chiu³

¹Department of Electrical Engineering, City University of Hong Kong, Hong Kong Special Administrative Region of China, People's Republic of China.

Physics in medicine and biology

|August 2, 2024

概括

一个新的深度学习模型BASC-Net从MRI扫描中准确地分割前列腺区域 (外围区域和中枢腺). 这种改进的细分有助于通过突出区域边界来检测前列腺癌病变.

科学领域:

医学成像分析分析医学成像分析
机器学习用于医疗保健
前列腺癌诊断的诊断方法

背景情况:

从MRI中精确细分前列腺区域 (外围区域[PZ]和中枢腺[CG]) 对于前列腺癌的诊断至关重要,因为病变的特征不同.
目前的细分方法难以准确地定位PZ和CG边界.

研究的目的:

开发一个先进的深度学习模型,边界意识的语义聚类网络 (BASC-Net),以改进前列腺区域的自动细分.
为了提高MRI扫描中PZ和CG边界定位的准确性.

主要方法:

在BASC-Net中,使用语义聚类注意 (SCA) 模块从内体和边界子区域提取特征.
一个边界感知对比 (BAC) 损失函数整合了同一区域内的子区域的特征,并区分了区域之间的特征.
SCA是第一个自我注意算法,用于特征基础构建,由地面真实面具监督.

主要成果:

与NCI-ISBI 2013挑战和前列腺158数据集中的九种最先进的方法相比,BASC-Net实现了更高的性能.
在NCI-ISBI上达到79.9% (PZ) 和88.6% (CG) 的子相似系数,在前列腺上达到80.5% (PZ) 和89.2% (CG).
在跨数据集评估中,证明了73.2% (PZ) 和87.4% (CG) 的概括性.

结论:

BASC-Net显著改善了自动细分前列腺区域边界从MRI.
精确的区域边界细分有助于在PZ和CG中更精确地检测前列腺癌病变.

关键词:

边界意识的对比 (BAC) 损失.前列腺区域细分前列腺区域细分自己注意力自我注意力语义聚类注意力 (SCA)

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