Novel Image Analysis Approach Quantifies Morphological Characteristics of 3D Breast Culture Acini with Varying Metastatic Potentials

Prognosis of breast cancer is primarily predicted by the histological grading of the tumor, where pathologists manually evaluate microscopic characteristics of the tissue. This labor intensive process suffers from intra- and inter-observer variations; thus, computer-aided systems that accomplish this assessment automatically are in high demand. We address this by developing an image analysis framework for the automated grading of breast cancer in in vitro three-dimensional breast epithelial acini through the characterization of acinar structure morphology. A set of statistically significant features for the characterization of acini morphology are exploited for the automated grading of six (MCF10 series) cell line cultures mimicking three grades of breast cancer along the metastatic cascade. In addition to capturing both expected and visually differentiable changes, we quantify subtle differences that pose a challenge to assess through microscopic inspection. Our method achieves 89.0% accuracy in grading the acinar structures as nonmalignant, noninvasive carcinoma, and invasive carcinoma grades. We further demonstrate that the proposed methodology can be successfully applied for the grading of in vivo tissue samples albeit with additional constraints. These results indicate that the proposed features can be used to describe the relationship between the acini morphology and cellular function along the metastatic cascade

NetMets: software for quantifying and visualizing errors in biological network segmentation

One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization

REGIONAL DIFFERENCES OF REACTIVE RESPONSES AGAINST SILICON NEURAL PROBE IMPLANTED INTO DEEP BRAIN REGIONS

This study was supported by the International Collaboration Program of NBS-ERC/KOSEF and NIH/NIBIB, R01- EB-000359

Cellular Responses to Micromachined Neuroprosthetic Device Insertion into the Brain

Insertion of prosthetic device is elicits reactive responses from both nervous tissue and vasculature that prevent successful integration of these devices. Their chronic use is limited due to glial encapsulation that electrically isolates devices from cellular networks. We examined time-dependent changes of reactive responses in neocortex, hippocampus, and thalamus using immunohistochemistry and confocal microscopy. Results show dramatic differences in the magnitude of cellular response in different brain regions and time-courses. These experiments will provide important new information for the design of improved biomaterials and nano/micro-device to control dynamic biological events in the central nervous system.This study was supported by the International Collaboration Program of NBSERC/ KOSEF and NIH/NIBIB, R01-EB- 000359