کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4334919 1614623 2015 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Efficient semi-automatic 3D segmentation for neuron tracing in electron microscopy images
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب (عمومی)
پیش نمایش صفحه اول مقاله
Efficient semi-automatic 3D segmentation for neuron tracing in electron microscopy images
چکیده انگلیسی


• Proposed a semi-automatic approach to proofreading segmentations of EM images.
• Achieved accuracy by experts approaching manual labeling quality.
• Showed a significant time reduction for dense segmentation of EM images by experts.

BackgroundIn the area of connectomics, there is a significant gap between the time required for data acquisition and dense reconstruction of the neural processes contained in the same dataset. Automatic methods are able to eliminate this timing gap, but the state-of-the-art accuracy so far is insufficient for use without user corrections. If completed naively, this process of correction can be tedious and time consuming.New methodWe present a new semi-automatic method that can be used to perform 3D segmentation of neurites in EM image stacks. It utilizes an automatic method that creates a hierarchical structure for recommended merges of superpixels. The user is then guided through each predicted region to quickly identify errors and establish correct links.ResultsWe tested our method on three datasets with both novice and expert users. Accuracy and timing were compared with published automatic, semi-automatic, and manual results.Comparison with existing methodsPost-automatic correction methods have also been used in Mishchenko et al. (2010) and Haehn et al. (2014). These methods do not provide navigation or suggestions in the manner we present. Other semi-automatic methods require user input prior to the automatic segmentation such as Jeong et al. (2009) and Cardona et al. (2010) and are inherently different than our method.ConclusionUsing this method on the three datasets, novice users achieved accuracy exceeding state-of-the-art automatic results, and expert users achieved accuracy on par with full manual labeling but with a 70% time improvement when compared with other examples in publication.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Neuroscience Methods - Volume 246, 15 May 2015, Pages 13–21
نویسندگان
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