Medical image segmentation
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In our paper, we present a performance evaluation of image segmentation algorithms on microscopic image data. In spite of the existence of many algorithms for image data partitioning, there is no universal and 'the best' method yet. Moreover, images of microscopic samples can be of various character and quality which can negatively influence the performance of image segmentation algorithms. Thus, the issue of selecting suitable method for a given set of image data is of big interest. We carried out a large number of experiments with a variety of segmentation methods to evaluate the behaviour of individual approaches on the testing set of microscopic images (cross-section images taken in three different modalities from the field of art restoration). The segmentation results were assessed by several indices used for measuring the output quality of image segmentation algorithms. In the end, the benefit of segmentation combination approach is studied and applicability of achieved results on another representatives of microscopic data category - biological samples - is shown.
- MeSH
- algoritmy * MeSH
- mikroskopie * metody MeSH
- myši MeSH
- počítačové zpracování obrazu metody normy MeSH
- zvířata MeSH
- Check Tag
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- hodnotící studie MeSH
- práce podpořená grantem MeSH
- MeSH
- biomechanika MeSH
- diagnostické zobrazování * metody využití MeSH
- lidé MeSH
- mechanické jevy MeSH
- počítačová rentgenová tomografie metody využití MeSH
- počítačové zpracování obrazu metody využití MeSH
- projekty vizualizace člověka MeSH
- statistika jako téma MeSH
- teoretické modely * MeSH
- tkáně fyziologie MeSH
- zobrazování trojrozměrné * metody využití MeSH
- Check Tag
- lidé MeSH
- MeSH
- algoritmy MeSH
- financování organizované MeSH
- intrakraniální subdurální hematom diagnóza MeSH
- lidé MeSH
- magnetická rezonanční tomografie metody využití MeSH
- Markovovy řetězce MeSH
- počítačová rentgenová tomografie metody využití MeSH
- počítačové zpracování obrazu MeSH
- teoretické modely MeSH
- Check Tag
- lidé MeSH
This paper describes an evaluation framework that allows a standardized and quantitative comparison of IVUS lumen and media segmentation algorithms. This framework has been introduced at the MICCAI 2011 Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop, comparing the results of eight teams that participated. We describe the available data-base comprising of multi-center, multi-vendor and multi-frequency IVUS datasets, their acquisition, the creation of the reference standard and the evaluation measures. The approaches address segmentation of the lumen, the media, or both borders; semi- or fully-automatic operation; and 2-D vs. 3-D methodology. Three performance measures for quantitative analysis have been proposed. The results of the evaluation indicate that segmentation of the vessel lumen and media is possible with an accuracy that is comparable to manual annotation when semi-automatic methods are used, as well as encouraging results can be obtained also in case of fully-automatic segmentation. The analysis performed in this paper also highlights the challenges in IVUS segmentation that remains to be solved.
- MeSH
- databáze faktografické normy MeSH
- internacionalita MeSH
- interpretace obrazu počítačem metody normy MeSH
- intervenční ultrasonografie metody normy MeSH
- lidé MeSH
- nemoci koronárních tepen ultrasonografie MeSH
- referenční hodnoty MeSH
- reprodukovatelnost výsledků MeSH
- senzitivita a specificita MeSH
- směrnice pro lékařskou praxi jako téma * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
new approach to the segmentation of 3D CT images is proposed in an attempt to provide texture-based segmentation of organs or disease diagnosis. 3D extension of Haralick texture features was studied calculating co-occurrences of all voxels in a small cubic region around the voxel. RESULTS: For verification, the proposed method was tested on a set of abdominal 3D volumes of patients. Statistically, the improvement in segmentation was significant for most of the organs considered herein. CONCLUSIONS: The proposed method has potential application in medical image segmentation, including diagnosis of diseases.
- MeSH
- algoritmy MeSH
- lidé MeSH
- počítačová rentgenová tomografie metody MeSH
- rentgendiagnostika břicha metody MeSH
- rentgenový obraz - interpretace počítačová metody MeSH
- reprodukovatelnost výsledků MeSH
- rozpoznávání automatizované metody MeSH
- senzitivita a specificita MeSH
- umělá inteligence MeSH
- vylepšení rentgenového snímku metody MeSH
- zobrazování trojrozměrné metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH
- MeSH
- algoritmy MeSH
- artefakty MeSH
- diagnostické zobrazování metody využití MeSH
- lidé MeSH
- magnetická rezonanční tomografie metody normy využití MeSH
- mozek anatomie a histologie MeSH
- počítačové zpracování obrazu MeSH
- počítačové zpracování signálu MeSH
- statistika jako téma MeSH
- teoretické modely MeSH
- zobrazování trojrozměrné metody využití MeSH
- Check Tag
- lidé MeSH
2nd ed. xviii, 826 s. : il. ; 25 cm
- MeSH
- diagnostické zobrazování MeSH
- počítačové zpracování obrazu MeSH
- Publikační typ
- příručky MeSH
- Konspekt
- Speciální počítačové metody. Počítačová grafika
- NLK Obory
- lékařská informatika
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse.
