Ting Chen

A method of segmenting images of biological specimens using adaptive classification to segment a biological specimen into different types of tissue regions. The segmentation is performed by, first, extracting features from the neighborhood of a grid of points (GPs) sampled on the whole-slide (WS) image and classifying them into different tissue types. Secondly, an adaptive classification procedure is performed where some or all of the GPs in a WS image are classified using a pre-built training… Show more

A method of segmenting images of biological specimens using adaptive classification to segment a biological specimen into different types of tissue regions. The segmentation is performed by, first, extracting features from the neighborhood of a grid of points (GPs) sampled on the whole-slide (WS) image and classifying them into different tissue types. Secondly, an adaptive classification procedure is performed where some or all of the GPs in a WS image are classified using a pre-built training database, and classification confidence scores for the GPs are generated. The classified GPs with high confidence scores are utilized to generate an adaptive training database, which is then used to re-classify the low confidence GPs. The motivation of the method is that the strong variation of tissue appearance makes the classification problem more challenging, while good classification results are obtained when the training and test data origin from the same slide. Show less

The present invention relates to systems and methods for adaptively optimizing broadband reference spectra for a multi-spectral image or adaptively optimizing reference colors for a bright-field image. The methods and systems of the present invention involve optimization techniques that are based on structures detected in an unmixed channel of the image, and involves detecting and segmenting structures from a channel, updating a reference matrix with signals estimated from the structures… Show more

The present invention relates to systems and methods for adaptively optimizing broadband reference spectra for a multi-spectral image or adaptively optimizing reference colors for a bright-field image. The methods and systems of the present invention involve optimization techniques that are based on structures detected in an unmixed channel of the image, and involves detecting and segmenting structures from a channel, updating a reference matrix with signals estimated from the structures, subsequently unmixing the image using the updated reference matrix, and iteratively repeating the process until an optimized reference matrix is achieved. Show less

The present invention involves a computer-implemented unmixing algorithm that employs a least-square method involving image patches, and a computer-implemented unmixing or color deconvolution method that incorporates spatial smoothness and structure continuity constraints, for example, into a neighborhood graph regularizer (i.e., using a graph to enforce the pixel value similarities for those pixels in the same neighborhood).

Systems and/or methods described herein relate to unmixing more than three stains, while preserving the biological constraints of the biomarkers. Unlimited numbers of markers may be unmixed from a limited-channel image, such as an RGB image, without adding any mathematical complicity to the model. Known co-localization information of different biomarkers within the same tissue section enables defining fixed upper bounds for the number of stains at one pixel. A group sparsity model may be… Show more

Systems and/or methods described herein relate to unmixing more than three stains, while preserving the biological constraints of the biomarkers. Unlimited numbers of markers may be unmixed from a limited-channel image, such as an RGB image, without adding any mathematical complicity to the model. Known co-localization information of different biomarkers within the same tissue section enables defining fixed upper bounds for the number of stains at one pixel. A group sparsity model may be leveraged to explicitly model the fractions of stain contributions from the co-localized biomarkers into one group to yield a least squares solution within the group. A sparse solution may be obtained among the groups to ensure that only a small number of groups with a total number of stains being less than the upper bound are activated. Show less

This disclosure describes methods, kits, and systems for scoring the immune response to cancer through examination of tissue infiltrating lymphocytes (TILs). Methods of scoring the immune response in cancer using tissue infiltrating lymphocytes include detecting CD3, CD8, CD20, and FoxP3 within the sample and scoring the detection manually or scoring the digital images of the staining with the aid of image analysis and algorithms.