Randy J. Giedt; Peter D. Koch; Ralph Weissleder

“ Of course, overlapping cells with homogenous fluorescence will require additional processing methods not utilized in this work in order to distinguish individual borders. Overall, this algorithm provides a framework for the analysis of single cell behavior in intravital imaging applications, an emerging area of biological research with currently only very limited methods available. This framework also provides a method of cell segmentation that could be widely adapted to other applications outside of intravital microscopy where data analysis has traditionally proven difficult. ”

“ In addition, despite the best efforts of most current methods of image registration [33] , some degree of animal movement may also occur during the course of intravital imaging sessions. This makes the acquisition of consistent data from the same subset of cells challenging. One solution to ensuring the continuity of data is to utilize robust tracking software to log the centroids of cells. ”

“ The final threshold of the image is then determined by minimizing the index of fuzziness, as defined by the foreground and background pixel distributions. Object attribute methods generally show improved performance on images where a global threshold proves to be unsatisfactory due to their selection of object features, rather than global intensity levels, in the image. Ray's method [10] is an example of a locally adaptive thresholding method. Locally adaptive methods typically provide superior results to methods proposing global thresholds. ”

“ Within thresholding methods, a number of approaches have already been described [7] , including: (i) histogram shape-based methods which function by analyzing histogram peaks and curves; (ii) clustering based methods that divide pixels into two groups, foreground and background, via analysis of global intensity values, to create a segmented image (iii) entropy based methods which utilize the entropic properties of the image foreground and background to segment the image, (iv) object attribute methods that look for commonalities among certain object features for image segmentation ”

“ As demonstrated in this work, the algorithm provides excellent results when dealing with dense cell fields, a scenario where most traditional thresholding methods have the greatest problems. Specifically, the use of a local adaptive method in segmenting cells is helpful in dealing with dense or overlapping cell areas due to its “local” thresholding approach, since cells of interest in such areas typically exhibit multiple levels of fluorescent intensity. ”

“ To do so, we modified a variance nonuniformity measure (VNU) , which typically assumes that well segmented images will have a uniform fluorescent intensity value [29] , [30] . This assumption, however, is unlikely to hold true for intravital images containing multiple levels of fluorescent intensity. Our contrast measure was therefore calculated as,
assuming σT is the variance of the pixels in the thresholded image, σM is the variance of the manually thresholded image and FM represents the foreground pixels of the manually thresholded image. ”

“ In general, these settings precluded typical issues such as background saturation of the drug signal or high rates of photobleaching that would significantly alter drug concentration measurements in vivo.
Results of using the Ray Thresholding Method in 3-D Models
In addition to thresholding individual images, a critical component of tracking objects is to have consistency in results over the course of a data set (i.e., the thresholding method should produce robust results despite various imaging defects occurring throughout a video) . ”

“ B. The average and standard deviation of nuclear drug concentration in all cells over time was analyzed using the described segmentation algorithm. The vessel concentration dynamics were also analyzed by quantifying drug channel fluorescence within an area of the vessel. C. The number of cells receiving a therapeutic dose of the drug over time. Single Cell Tracking Over Time
Tumor and host cells are often quite mobile and can move over considerable distances during a several hour imaging session. ”

“ B. Manual tracking of arbitrarily selected cells. C. By combining results using the segmentation algorithm together with the tracking data, drug concentration over time in 10 sample cells could be plotted. Discussion
Automating microscopic image analysis of time-series (both 2D and 3D) is currently a significant bottleneck in the analysis of in vivo drug distribution and function at the population, single cell and intracellular level. In a bid to alleviate this problem, an increasing number of companion imaging drugs are being developed to study how drugs behave and/or fail. ”

“ Figure S3 Detailed view of overall morphological operations and object labeling on an example image. This image was thresholded using Ray's method as described, followed by standard morphological operations to remove artifacts produced by the thresholding process. A rainbow color labeled image is presented to show distinct objects recognized by the analysis program. (TIFF)
Click here for additional data file. Figure S4 (I–III) Detailed views of cells with heterogeneous fluorescence (indicated by arrows) and segmentation of these areas via the reported algorithm. ”

“ Nude mice (Cox7, Massachusetts General Hospital) were surgically implanted with a dorsal skin window chamber. Approximately 3–4×106 cells, suspended in 1∶1 phosphate buffered saline (PBS) and Matrigel (BD Biosciences, Franklin Lakes, NJ) , were implanted under the fascia and allowed to grow for ∼2 weeks. As soon as the tumors became vascularized and had reached 1–2 mm in size, mice were anesthetized with 2% isoflurane in 2 L/minute oxygen on a heated microscope stage. ”

“ A Survey on Evaluation Methods for Image Segmentation. Pattern Recogn 25: 1335–1346 ↑ Gonzalez RC, Woods RE, Eddins SL (2009) Digital Image processing using MATLAB. Knoxville, TN: Gatesmark Publishing.↑ Evers B, Drost R, Schut E, de Bruin M, van der Burg E, et al. (2008) Selective inhibition of BRCA2-deficient mammary tumor cell growth by AZD2281 and cisplatin. Clin Cancer Res 14: 3916–3925 pmid:18559613↑ Lee S, Vinegoni C, Feruglio PF, Fexon L, Gorbatov R, et al. (2012) Real-time in vivo imaging of the beating mouse heart at microscopic resolution. ”