Embodiments of the invention relate to tissue images and more specifically to classification of objects in the tissue images to extract content from the images.
Tissue classification at a cellular level is desirable for medical diagnostic or prognostic purposes. Typically, for diagnostic or prognostic purposes pathologists acquire tissue samples through biopsy, view them with microscopes at high resolutions, and manually analyze the images. Pathologists rely on training and experience to determine tissue types or obtain diagnostic or prognostic information from the tissue images. Depending on the type of information desired by a pathologist, different biomarker stains are used to view a tissue sample. Each stain provides a distinctive view of the tissue and contains distinct information about a portion of the tissue. Differences in the uptake of the biomarker stains by different types of cells and cell parts are used for differentiating and quantifying cells in the tissue. While the methods for tissue classification performed by the user (i.e., the pathologist) on images requiring analysis may be effective in classifying basic tissue types, these methods suffer from high inefficiency and user variability. Also, the manual method is unable to utilize the complex information that can be gained from viewing the tissue across multiple stains, and may lack reproducibility.
Digitization of images is used to reduce some of the inefficiency problems; however, despite the digitization of the images, the methods depend primarily on the ability of the user to produce fast, accurate, complex results.
By combining the information gathered using different stains, a more comprehensive picture of the tissue sample may be obtained. With image registration, images of different staining of the same tissue may be aligned to form a multiplex image. Using all of the distinctive data content from each stain, image segmentation may be performed at the intercellular and intracellular levels. The result of this process may provide a dataset containing quantitative information on the size, shape, and staining level of each cell, cell membrane, cell nucleus, and cell cytoplasm, for every stain. However, the image segmentation process may occasionally fail or misclassify cells, or may not be able to handle artifacts or out of focus images.
Given sufficient labeled training data, traditional classification techniques may be capable of developing classifiers that may be patient, gland, and tissue specific. With a proper annotation tool and real time classification, these classification techniques along with machine learning techniques may perform specific tasks in the clinical setting. However, these classification techniques are unable to manage the wide variety of gland configurations and tissue types.
Therefore, it is desirable to have an enhanced method of tissue segmentation and classification that is automated and more reliable, to enhance diagnostic and prognostic information and subsequent treatment and to reduce costs and errors in tissue classifications