SuRVoS: Super-Region Volume Segmentation workbench
| العنوان: | SuRVoS: Super-Region Volume Segmentation workbench |
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| المؤلفون: | Wah Chiu, Michele C. Darrow, Elizabeth Duke, Alun W. Ashton, Tony P. Pridmore, Ying Sun, Wei Dai, Andrew P. French, Matthew C. Spink, Imanol Luengo, Cynthia Y. He, Mark Basham |
| المصدر: | Journal of Structural Biology |
| بيانات النشر: | Elsevier, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | 0301 basic medicine, SVM, Support Vector Machines, SXT, Soft X-ray Tomography, Computer science, Interactive segmentation, Datasets as Topic, Scale-space segmentation, 02 engineering and technology, SLIC, Simple Iterative Linear Clustering, Article, Field (computer science), Machine Learning, RBF, Radial Basis Function, TV, Total Variation, 03 medical and health sciences, Software, ERF, Extremely Randomized Forest, RoI, Region of Interest, Structural Biology, Cryo electron tomography, Cryo soft X-ray tomography, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Segmentation, SEM, Scanning Electron Microscopy, Data Curation, SIRT, Simultaneous Iterative Reconstruction Tomography, MRF, Markov Random Field, TEM, Transmission Electron Microscopy, business.industry, Segmentation-based object categorization, Volume (computing), SuRVoS, Super-Region Volume Segmentation, Pattern recognition, Grid, SBF, Serial Block Face, Super-Regions, 030104 developmental biology, CCD, Charge-coupled Device, FIB, Focused Ion Beam, RF, Random Forest, Semi-supervised learning, Workbench, 020201 artificial intelligence & image processing, Artificial intelligence, business, Hierarchical segmentation, Algorithms |
| الوصف: | Segmentation of biological volumes is a crucial step needed to fully analyse their scientific content. Not having access to convenient tools with which to segment or annotate the data means many biological volumes remain under-utilised. Automatic segmentation of biological volumes is still a very challenging research field, and current methods usually require a large amount of manually-produced training data to deliver a high-quality segmentation. However, the complex appearance of cellular features and the high variance from one sample to another, along with the time-consuming work of manually labelling complete volumes, makes the required training data very scarce or non-existent. Thus, fully automatic approaches are often infeasible for many practical applications. With the aim of unifying the segmentation power of automatic approaches with the user expertise and ability to manually annotate biological samples, we present a new workbench named SuRVoS (Super-Region Volume Segmentation). Within this software, a volume to be segmented is first partitioned into hierarchical segmentation layers (named Super-Regions) and is then interactively segmented with the user's knowledge input in the form of training annotations. SuRVoS first learns from and then extends user inputs to the rest of the volume, while using Super-Regions for quicker and easier segmentation than when using a voxel grid. These benefits are especially noticeable on noisy, low-dose, biological datasets. |
| وصف الملف: | PDF; application/pdf |
| اللغة: | English |
| تدمد: | 1047-8477 1095-8657 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f72d4ac23c21d628fed7bada5efa5874Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....f72d4ac23c21d628fed7bada5efa5874 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 10478477 10958657 |
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