PDF(Pubmed)
Abstract：
UNASSIGNED: Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries.
UNASSIGNED: We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples.
UNASSIGNED: Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the K-means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes.
UNASSIGNED: The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised K-means algorithm. The unsupervised K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas\' unique endmembers produced by the two methods agree with each other within <2% residual error margin.
UNASSIGNED: Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas\' unique endmembers used by the two methods agree to <2% residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification.
UNASSIGNED: We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples.
UNASSIGNED: Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the K-means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes.
UNASSIGNED: The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised K-means algorithm. The unsupervised K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas\' unique endmembers produced by the two methods agree with each other within <2% residual error margin.
UNASSIGNED: Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas\' unique endmembers used by the two methods agree to <2% residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification.
摘要：
■高光谱暗场显微镜（HSDFM）和数据立方体分析算法证明了对各种组织类型的成功检测和分类，包括在保乳手术中切除的人类乳房肿瘤切除术后乳腺组织中的癌区域。
■我们将HSDFM的应用扩展到组织病理学前人类乳腺肿块切除术样本中组织类型和肿瘤亚型的分类。
■在保乳手术期间切除的乳腺组织通过HSDFM成像并分析。通过将聚苯乙烯微珠溶液的反向散射强度光谱与实验数据的蒙特卡罗模拟进行比较来评估HSDFM的性能。对于分类算法，两种分析方法，应用基于光谱角度映射器（SAM）算法的监督技术和基于K-means算法的无监督技术对包括癌亚型在内的各种组织类型进行分类。在监督技术中，使用由H&E注释指导的手动提取端元的SAM算法作为参考光谱，允许分割图与分类的组织类型，包括癌亚型。
■手动提取的已知组织类型的端成员及其相应的阈值光谱相关角进行分类，是一个很好的参考库，可以验证由无监督K-means算法计算的端成员。无监督K-means算法，没有先验信息，产生具有各种组织类型的主要端成员的丰度图，包括浸润性导管癌和浸润性黏液癌的癌亚型。通过两种方法产生的两种独特的终元在<2％的残余误差范围内彼此一致。
■我们的报告展示了一种用于验证无监督算法的强大程序，该算法具有基于地面实况的必要参数集，组织病理学信息。我们已经证明，组织病理学指导的末端成员的经过训练的库以及针对明确定义的参考数据立方体计算的相关阈值光谱相关角服务于此类参数。两种分类算法，监督和无监督算法，用于识别组织中存在的浸润性导管癌和浸润性粘液性癌的癌亚型的区域。两种方法使用的两种癌的独特末端成员一致<2％的残余误差范围。这个高质量的库在没有环境背景的环境下收集，可能有助于开发或验证更先进的无监督数据立方体分析算法。例如用于有效子类型分类的有效神经网络。
■我们将HSDFM的应用扩展到组织病理学前人类乳腺肿块切除术样本中组织类型和肿瘤亚型的分类。
■在保乳手术期间切除的乳腺组织通过HSDFM成像并分析。通过将聚苯乙烯微珠溶液的反向散射强度光谱与实验数据的蒙特卡罗模拟进行比较来评估HSDFM的性能。对于分类算法，两种分析方法，应用基于光谱角度映射器（SAM）算法的监督技术和基于K-means算法的无监督技术对包括癌亚型在内的各种组织类型进行分类。在监督技术中，使用由H&E注释指导的手动提取端元的SAM算法作为参考光谱，允许分割图与分类的组织类型，包括癌亚型。
■手动提取的已知组织类型的端成员及其相应的阈值光谱相关角进行分类，是一个很好的参考库，可以验证由无监督K-means算法计算的端成员。无监督K-means算法，没有先验信息，产生具有各种组织类型的主要端成员的丰度图，包括浸润性导管癌和浸润性黏液癌的癌亚型。通过两种方法产生的两种独特的终元在<2％的残余误差范围内彼此一致。
■我们的报告展示了一种用于验证无监督算法的强大程序，该算法具有基于地面实况的必要参数集，组织病理学信息。我们已经证明，组织病理学指导的末端成员的经过训练的库以及针对明确定义的参考数据立方体计算的相关阈值光谱相关角服务于此类参数。两种分类算法，监督和无监督算法，用于识别组织中存在的浸润性导管癌和浸润性粘液性癌的癌亚型的区域。两种方法使用的两种癌的独特末端成员一致<2％的残余误差范围。这个高质量的库在没有环境背景的环境下收集，可能有助于开发或验证更先进的无监督数据立方体分析算法。例如用于有效子类型分类的有效神经网络。
