PT  - JOURNAL ARTICLE
AU  - Kamal L Nahas
AU  - João Ferreira Fernandes
AU  - Colin Crump
AU  - Stephen Graham
AU  - Maria Harkiolaki
TI  - <em>Contour</em>, a semi-automated segmentation and quantitation tool for cryo-soft-X-ray tomography
AID  - 10.1101/2021.12.03.470962
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.12.03.470962
4099  - http://biorxiv.org/content/early/2021/12/04/2021.12.03.470962.short
4100  - http://biorxiv.org/content/early/2021/12/04/2021.12.03.470962.full
AB  - Cryo-soft-X-ray tomography is being increasingly used in biological research to study the morphology of cellular compartments and how they change in response to different stimuli, such as viral infections. Segmentation of these compartments is limited by time-consuming manual tools or machine learning algorithms that require extensive time and effort to train. Here we describe Contour, a new, easy-to-use, highly automated segmentation tool that enables accelerated segmentation of tomograms to delineate distinct cellular compartments. Using Contour, cellular structures can be segmented based on their projection intensity and geometrical width by applying a threshold range to the image and excluding noise smaller in width than the cellular compartments of interest. This method is less laborious and less prone to errors from human judgement than current tools that require features to be manually traced, and does not require training datasets as would machine-learning driven segmentation. We show that high-contrast compartments such as mitochondria, lipid droplets, and features at the cell surface can be easily segmented with this technique in the context of investigating herpes simplex virus 1 infection. Contour can extract geometric measurements from 3D segmented volumes, providing a new method to quantitate cryo-soft-X-ray tomography data. Contour can be freely downloaded at github.com/kamallouisnahas/Contour.Impact Statement More research groups are using cryo-soft-X-ray tomography as a correlative imaging tool to study the ultrastructure of cells and tissues but very few tomograms are segmented with existing segmentation programs. Segmentation is usually a prerequisite for measuring the geometry of features in tomograms but the time- and labour-intensive nature of current segmentation techniques means that such measurements are rarely across a large number of tomograms, as is required for robust statistical analysis. Contour has been designed to facilitate the automation of segmentation and, as a result, reduce manual effort and increase the number of tomograms that can be segmented. Because it requires minimal manual intervention, Contour is not as prone to human error as programs that require the users to trace the edges of cellular features. Geometry measurements of the segmented volumes can be calculated using this program, providing a new platform to quantitate cryoSXT data. Contour also supports quantitation of volumes imported from other segmentation programs. The generation of a large sample of segmented volumes with Contour that can be used as a representative training dataset for machine learning applications is a long-term aspiration of this technique.Competing Interest StatementThe authors have declared no competing interest.