كتاب
Graph-based pancreatic islet segmentation for early type 2 diabetes mellitus on histopathological tissue
| العنوان: | Graph-based pancreatic islet segmentation for early type 2 diabetes mellitus on histopathological tissue |
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| المؤلفون: | Floros, X, Fuchs, T J, Rechsteiner, M P, Spinas, G, Moch, H, Buhmann, J M |
| المساهمون: | Yang, G Z, Hawkes, D, Rueckert, D, Noble, A, Taylor, C, Yang, G Z ( G Z ), Hawkes, D ( D ), Rueckert, D ( D ), Noble, A ( A ), Taylor, C ( C ) |
| المصدر: | Floros, X; Fuchs, T J; Rechsteiner, M P; Spinas, G; Moch, H; Buhmann, J M (2009). Graph-based pancreatic islet segmentation for early type 2 diabetes mellitus on histopathological tissue. In: Yang, G Z; Hawkes, D; Rueckert, D; Noble, A; Taylor, C. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part 2. Berlin: Springer, 633-640. |
| بيانات النشر: | Springer |
| سنة النشر: | 2009 |
| المجموعة: | University of Zurich (UZH): ZORA (Zurich Open Repository and Archive |
| مصطلحات موضوعية: | Clinic for Endocrinology and Diabetology, Institute of Pathology and Molecular Pathology, 610 Medicine & health |
| الوصف: | It is estimated that in 2010 more than 220 million people will be affected by type 2 diabetes mellitus (T2DM). Early evidence indicates that specific markers for alpha and beta cells in pancreatic islets of Langerhans can be used for early T2DM diagnosis. Currently, the analysis of such histological tissues is manually performed by trained pathologists using a light microscope. To objectify classification results and to reduce the processing time of histological tissues, an automated computational pathology framework for segmentation of pancreatic islets from histopathological fluorescence images is proposed. Due to high variability in the staining intensities for alpha and beta cells, classical medical imaging approaches fail in this scenario. The main contribution of this paper consists of a novel graph-based segmentation approach based on cell nuclei detection with randomized tree ensembles. The algorithm is trained via a cross validation scheme on a ground truth set of islet images manually segmented by 4 expert pathologists. Test errors obtained from the cross validation procedure demonstrate that the graph-based computational pathology analysis proposed is performing competitively to the expert pathologists while outperforming a baseline morphological approach. |
| نوع الوثيقة: | book part |
| وصف الملف: | application/pdf |
| اللغة: | German |
| ردمك: | 978-3-642-04270-6 3-642-04270-8 |
| تدمد: | 0302-9743 |
| العلاقة: | https://www.zora.uzh.ch/id/eprint/27430/10/2009.miccai.islet-V.pdfTest; urn:isbn:978-3-642-04270-6; urn:issn:0302-9743 (P) 1611-3349 (E) |
| DOI: | 10.5167/uzh-27430 |
| DOI: | 10.1007/978-3-642-04271-3_77 |
| الإتاحة: | https://doi.org/10.5167/uzh-2743010.1007/978-3-642-04271-3_77Test https://www.zora.uzh.ch/id/eprint/27430Test/ https://www.zora.uzh.ch/id/eprint/27430/10/2009.miccai.islet-V.pdfTest |
| حقوق: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.2FB0BE7E |
| قاعدة البيانات: | BASE |
| ردمك: | 9783642042706 3642042708 |
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| تدمد: | 03029743 |
| DOI: | 10.5167/uzh-27430 |