Dataset Information

Enabling user-guided segmentation and tracking of surface-labeled cells in time-lapse image sets of living tissues.

ABSTRACT: To study the process of morphogenesis, one often needs to collect and segment time-lapse images of living tissues to accurately track changing cellular morphology. This task typically involves segmenting and tracking tens to hundreds of individual cells over hundreds of image frames, a scale that would certainly benefit from automated routines; however, any automated routine would need to reliably handle a large number of sporadic, and yet typical problems (e.g., illumination inconsistency, photobleaching, rapid cell motions, and drift of focus or of cells moving through the imaging plane). Here, we present a segmentation and cell tracking approach based on the premise that users know their data best-interpreting and using image features that are not accounted for in any a priori algorithm design. We have developed a program, SeedWater Segmenter, that combines a parameter-less and fast automated watershed algorithm with a suite of manual intervention tools that enables users with little to no specialized knowledge of image processing to efficiently segment images with near-perfect accuracy based on simple user interactions.

SUBMITTER: Mashburn DN

PROVIDER: S-EPMC3331924 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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Enabling user-guided segmentation and tracking of surface-labeled cells in time-lapse image sets of living tissues.

Mashburn David N DN Lynch Holley E HE Ma Xiaoyan X Hutson M Shane MS

Cytometry. Part A : the journal of the International Society for Analytical Cytology 20120312 5

To study the process of morphogenesis, one often needs to collect and segment time-lapse images of living tissues to accurately track changing cellular morphology. This task typically involves segmenting and tracking tens to hundreds of individual cells over hundreds of image frames, a scale that would certainly benefit from automated routines; however, any automated routine would need to reliably handle a large number of sporadic, and yet typical problems (e.g., illumination inconsistency, phot ...[more]

PMID: 22411907

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Project description:On?board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real?time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image?guided radiotherapy (MR?IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k?means (FKM), k?harmonic means (KHM), and reaction?diffusion level set evolution (RD?LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR?TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR?TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD?LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP?TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high?contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR?TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on?board MR?IGRT system. PACS number(s): 87.57.nm, 87.57.N?, 87.61.Tg

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Dataset Information

Enabling user-guided segmentation and tracking of surface-labeled cells in time-lapse image sets of living tissues.

Publications

Enabling user-guided segmentation and tracking of surface-labeled cells in time-lapse image sets of living tissues.

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