Endoscopic image analysis of aberrant crypt foci

Tese de Mestrado Integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

Identification of histological features to predict MUC2 expression in colon cancer tissues

Colorectal cancer (CRC) is the third-most common form of cancer among Americans. Like normal colon tissue, CRC cells are sustained by a subpopulation of “stem cells” that possess the ability to self-renew and differentiate into more specialized cancer cell types. In normal colon tissue, the enterocytes, goblet cells and other epithelial cells in the mucosa region have distinct morphologies that distinguish them from the other cells in the lamina propria, muscularis mucosa, and submucosa. However, in a tumor, the morphology of the cancer cells varies dramatically. Cancer cells that express genes specific to goblet cells significantly differ in shape and size compared to their normal counterparts. Even though a large number of hematoxylin and eosin (H&E)-stained sections and the corresponding RNA sequencing (RNASeq) data from CRC are available from The Cancer Genome Atlas (TCGA), prediction of gene expression patterns from tissue histological features has not been attempted yet. In this manuscript, we identified histological features that are strongly associated with MUC2 expression patterns in a tumor. Specifically, we show that large nuclear area is associated with MUC2-high tumors (p < 0.001). This discovery provides insight into cancer biology and tumor histology and demonstrates that it may be possible to predict certain gene expressions from histological features

Micro-Net: A unified model for segmentation of various objects in microscopy images

Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy images. The proposed network can be used to segment cells, nuclei and glands in fluorescence microscopy and histology images after slight tuning of input parameters. The network trains at multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The extra convolutional layers which bypass the max-pooling operation allow the network to train for variable input intensities and object size and make it robust to noisy data. We compare our results on publicly available data sets and show that the proposed network outperforms recent deep learning algorithms

Identification of histological features to predict MUC2 expression in colon cancer tissues

Colorectal cancer (CRC) is the third-most common form of cancer among Americans. Like normal colon tissue, CRC cells are sustained by a subpopulation of “stem cells” that possess the ability to self-renew and differentiate into more specialized cancer cell types. In normal colon tissue, the enterocytes, goblet cells and other epithelial cells in the mucosa region have distinct morphologies that distinguish them from the other cells in the lamina propria, muscularis mucosa, and submucosa. However, in a tumor, the morphology of the cancer cells varies dramatically. Cancer cells that express genes specific to goblet cells significantly differ in shape and size compared to their normal counterparts. Even though a large number of hematoxylin and eosin (H&E)-stained sections and the corresponding RNA sequencing (RNASeq) data from CRC are available from The Cancer Genome Atlas (TCGA), prediction of gene expression patterns from tissue histological features has not been attempted yet. In this manuscript, we identified histological features that are strongly associated with MUC2 expression patterns in a tumor. Specifically, we show that large nuclear area is associated with MUC2-high tumors (p < 0.001). This discovery provides insight into cancer biology and tumor histology and demonstrates that it may be possible to predict certain gene expressions from histological features

Confocal Microscopy and Nuclear Segmentation Algorithm for Quantitative Imaging of Epithelial Tissue

Carcinomas, cancers that originate in the epithelium, account for more than 80% of all cancers. When detected early, the 5-year survival rate is greatly increased. Biopsy and histopathology is the current gold standard for diagnosis of epithelial carcinomas which is an invasive, time-intensive, and stressful procedure. In vivo confocal microscopy has the potential to non-invasively image epithelial tissue in near-real time. This dissertation describes the development of a confocal microscope for imaging epithelial tissues and an image processing algorithm for segmentation of epithelial nuclei. A rapid beam and stage scanning combination was used to acquire fluorescence confocal images of cellular and tissue features along the length of excised mouse colon. A single 1 × 60 mm2 field of view is acquired in 10 seconds. Disruption of crypt structure such as size, shape, and distribution is visualized in images of inflamed colon tissue, while the normal mouse colon exhibited uniform crypt structure and distribution. An automated pulse coupled neural network segmentation algorithm was developed for epithelial nuclei segmentation. An increase in nuclear size and the nuclear-to-cytoplasmic ratio is a potential precursor to pre-cancer development. The spiking cortical model algorithm was evaluated using a developed confocal image model of epithelial tissues with varying contrast. It was further validated on reflectance confocal images of porcine and human oral tissue from two separate confocal imaging systems. Biopsies of human oral mucosa are used to determine the tissue and system effects on measurements of nuclear-to-cytoplasmic ratio