Bubble segmentation based on Shape From Shading for in-situ microscopy

Martínez, Geovanni; Frerichs, Jan-Gerd; Scheper, Thomas

doi:10.1109/conielecomp.2011.5749321

Cited by 2 publications

(1 citation statement)

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“…The method accounts for morphological changes that are induced by cell growth. A method for segmenting cells for in-situ microscopy is presented in Martinez et al [36]. Here the instrument is positioned inside a bioreactor in order to monitor cell culture processes.…”

Section: Model-based Methodsmentioning

confidence: 99%

Epithelial Cell Segmentation via Shape Ranking

Santamaría-Pang

Huang

Pang

et al. 2014

Lecture Notes in Computational Vision and Biomechanics

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We present a robust and high-throughput computational method for cell segmentation using multiplexed immunohistopathology images. The major challenges in obtaining an accurate cell segmentation from tissue samples are due to (i) complex cell and tissue morphology, (ii) different sources of variability including non-homogeneous staining and microscope specific noise, and (iii) tissue quality. Here we present a fast method that uses cell shape and scale information via unsupervised machine learning to enhance and improve general purpose segmentation methods. The proposed method is well suited for tissue cytology because it captures the the morphological and shape heterogeneity in different cell populations. We discuss our segmentation framework for analysing approximately one hundred images of lung and colon cancer and we restrict our analysis to epithelial cells.

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Section: Model-based Methodsmentioning

confidence: 99%