Next week we continue our Joint Applied Mathematics and Statistics Seminar 

On Thursday, Nov. 26th., Sean Robinson will give a talk starting at 10:15 at room M1 (Quantum).

Title:  Segmentation of image data from 3D in vitro co-culture models with Markov random fields

Abstract:

The complexity of 3D in vitro cell culture models has so far limited their successful implementation for phenotypic screening and other biological applications. There is a great need for accurate and robust image segmentation tools to facilitate the analysis of biologically relevant 3D cell culture models. We present a segmentation methodology based on Markov random fields (MRFs) for 3D image data from spinning-disc confocal microscopy. The 3D segmentation output provides valuable insights into the complex architecture of 3D in vitro cell culture models of prostate cancer. By using our MRF based segmentation  methodology it is possible to investigate if tumour cells and cancer-associated fibroblasts are in direct contact or are physically separated. This is important for tumour biology in general and also verifies that the 3D model recapitulates relevant aspects of cancer tissue.

All interested are warmly welcome!

Next week we continue our Joint Applied Mathematics and Statistics Seminar 

On Thursday, Nov. 26th., Sean Robinson will give a talk starting at 10:15 at room M1 (Quantum).

Title:  Segmentation of image data from 3D in vitro co-culture models with Markov random fields

Abstract:

The complexity of 3D in vitro cell culture models has so far limited their successful implementation for phenotypic screening and other biological applications. There is a great need for accurate and robust image segmentation tools to facilitate the analysis of biologically relevant 3D cell culture models. We present a segmentation methodology based on Markov random fields (MRFs) for 3D image data from spinning-disc confocal microscopy. The 3D segmentation output provides valuable insights into the complex architecture of 3D in vitro cell culture models of prostate cancer. By using our MRF based segmentation  methodology it is possible to investigate if tumour cells and cancer-associated fibroblasts are in direct contact or are physically separated. This is important for tumour biology in general and also verifies that the 3D model recapitulates relevant aspects of cancer tissue.

All interested are warmly welcome!