An automatic algorithm for the segmentation and morphological analysis of microvessels in immunostained histological tumour sections

Abstract

SummaryA fully automatic segmentation and morphological analysis algorithm for the analysis of microvessels from CD31 immunostained histological tumour sections is presented. Development of the algorithm exploited the distinctive hues of stained vascular endothelial cells, cell nuclei and background, to provide the seeds for a 'region-growing' method for object segmentation in the 3D hue, saturation, value (HSV) colour model. The segmented objects, identified as microvessels by CD31 immunostaining, were post-processed with three morphological tasks: joining separate objects that were likely to belong to a single vessel, closing objects that had a narrow gap around their periphery, and splitting objects with multiple lumina into individual vessels. The automatic segmentation was validated against a hand-segmented set of 44 images from three different SW1222 human colorectal carcinomas xenografted into mice. 96.3 ± 0.9% of pixels were found to be correctly classified. Automated segmentation was carried out on a further 53 images from three histologically distinct mouse fibrosarcomas (MFs) for morphological comparison with the SW1222 tumours. Four morphometric measurements were calculated for each segmented vessel: vascular area (VA), ratio of lumen area to vascular area (lu/VA), eccentricity (e), and roundness (ro). In addition, the total vascular area relative to tumour tissue area (rVA) was calculated. lu/VA, e and ro were found to be significantly smaller in MF tumours than in SW1222 tumours (p < 0.05; unpaired t-test). The algorithm is available through the website http://www.caiman.org.uk where images can be uploaded, processed and sent back to users. The output from CAIMAN consists of the original image with boundaries of segmented vessels overlaid, the Correspondence to:

Automatic segmentation of focal adhesions from mouse embryonic fibroblasts

“…The sensitivity analysis of Fig 7c was interesting as it was clear that the area of the lamellipodia was not sensitive to the variation, as opposed to the ratio of the major to minor axes. In future work we consider that a more thorough morphological analysis [7] of the focal adhesions, the cell periphery, the outer ring and the shape of the cell. In addition, the algorithm should be tested with more images as the samples were relatively small.…”

Online chromatic and scale-space microvessel-tracing analysis for transmitted light optical images

“…However, there are many other colour spaces, such as the YUV (luminance and chrominance) used for analogue colour television or CMYK (cyan, magenta yellow, black) used in printing. A common space used for biomedical image analysis is the Hue, Saturation and Value (HSV) (Maximova et al, 2006;Reyes-Aldasoro et al, 2011b;van Der Laak et al, 2000). The HSV space describes perceptual colour relationships related to the artistic ideas of hue, tint and shade (Gonzalez and Woods, 2008;Smith, 1978), which provide a more natural description of colour than additive or subtractive colour components.…”

“…A 3D chromatic histogram describing the chromatic characteristics of an image was developed previously for analysis of histological tissue sections (Reyes-Aldasoro et al, 2011b) and is implemented here for intravital imaging of tumours. We also implemented a scale-space ridge detection algorithm (Lindeberg, 1998a;Lindeberg, 1998b) as a fully automatic vessel tracing algorithm.…”

“…Quantification of the chromatic characteristics of an image can be performed on the numerical values of the channels, or on measures derived from them, such as histograms, which have been used in one (Schettini, 1993), two (Angulo and Serra, 2005;Angulo and Serra, 2007;Hanbury and Serra, 2003) and three dimensions (Cho et al, 2001;Reyes-Aldasoro et al, 2011b) in different biomedical applications.…”

Cell Image Processing Methods for Automatic Cell Pattern Recognition and Morphological Analysis of Mesenchymal Stem Cells - An Algorithm for Cell Classification and Adaptive Brightness Correction -

“…Morphological cell analysis has been integrated in new methods for biomedical applications, such as automatic segmentation and analysis of histological tumor sections (Reyes-Aldasoro et al, 2011;Cheng et al, 2010;Schildkraut et al, 2010), boundary detection of cervical cell nuclei considering overlapping and clustering (Plissiti et al, 2011), the granules segmentation and spatial distribution analysis (Diaz et al, 2010), and morphological characteristics analysis of specific biomedical cells (Brun et al, 2011;Amini et al, 2010;Xiong et al, 2010).…”