Image segmentation
Dotaz
Zobrazit nápovědu
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
Background: Breast cancer is one of the leading cancers in woman worldwide both in developed and developing nations as per the records from World Health Organization. Many studies have shown that mammography is very effective tool for the breast cancer diagnosis. Mass segmentation plays an important step for the cancer detection. Objective: The objective of the proposed method is to segment the mass and to classify the mass with high accuracy. Methods: The segmentation includes two main steps. First, a rough initial segmentation through iterative thresholding, and second, an active contour based segmentation. The relevant statistical features are extracted and the classification is done by using Adaptive Neuro Fuzzy Inference System (ANFIS). Results: The proposed mass detection scheme achieves sensitivity of 87.5% and specificity of 100% for a set of twenty two images. The overall segmentation accuracy obtained is 91.30%. Conclusions: This work appears to be of high clinical significance since the mass detection plays an important role in diagnosis of breast cancer.
- 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
The analysis and segmentation of articular cartilage magnetic resonance (MR) images belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal system of the knee area. Conventional regional segmentation methods, which are based either on the histogram partitioning (e.g., Otsu method) or clustering methods (e.g., K-means), have been frequently used for the task of regional segmentation. Such methods are well known as fast and well working in the environment, where cartilage image features are reliably recognizable. The well-known fact is that the performance of these methods is prone to the image noise and artefacts. In this context, regional segmentation strategies, driven by either genetic algorithms or selected evolutionary computing strategies, have the potential to overcome these traditional methods such as Otsu thresholding or K-means in the context of their performance. These optimization strategies consecutively generate a pyramid of a possible set of histogram thresholds, of which the quality is evaluated by using the fitness function based on Kapur's entropy maximization to find the most optimal combination of thresholds for articular cartilage segmentation. On the other hand, such optimization strategies are often computationally demanding, which is a limitation of using such methods for a stack of MR images. In this study, we publish a comprehensive analysis of the optimization methods based on fuzzy soft segmentation, driven by artificial bee colony (ABC), particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO), and a genetic algorithm for an optimal thresholding selection against the routine segmentations Otsu and K-means for analysis and the features extraction of articular cartilage from MR images. This study objectively analyzes the performance of the segmentation strategies upon variable noise with dynamic intensities to report a segmentation's robustness in various image conditions for a various number of segmentation classes (4, 7, and 10), cartilage features (area, perimeter, and skeleton) extraction preciseness against the routine segmentation strategies, and lastly the computing time, which represents an important factor of segmentation performance. We use the same settings on individual optimization strategies: 100 iterations and 50 population. This study suggests that the combination of fuzzy thresholding with an ABC algorithm gives the best performance in the comparison with other methods as from the view of the segmentation influence of additive dynamic noise influence, also for cartilage features extraction. On the other hand, using genetic algorithms for cartilage segmentation in some cases does not give a good performance. In most cases, the analyzed optimization strategies significantly overcome the routine segmentation methods except for the computing time, which is normally lower for the routine algorithms. We also publish statistical tests of significance, showing differences in the performance of individual optimization strategies against Otsu and K-means method. Lastly, as a part of this study, we publish a software environment, integrating all the methods from this study.
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.
Difuzně vážené zobrazení tkání je zásadní součástí vyšetřovacích protokolů magnetické rezonance. Snížení jeho kvality na 3T přístrojích v porovnání s 1, 5T je při použití konvenčních metod náběru dat významné a vyžaduje hledání nových postupů. Technika RESOLVE (REeadout Segmentation Of Long Variable Echo-trains) představuje volbu, která zmenšuje geometrické distorze a poskytuje vyšší prostorové rozlišení obrazu, nicméně při prodloužené době vyšetření.
Diffusion-weighted tissue imaging is very important part of magnetic resonance examination protocols. There is significant decrease of its quality in 3T compared to 1,5T machines if conventional techniques of data collections are used, and new methods are needed to be applied. RESOLVE (REadout Segmentation Of Long Variable Echo-trains) technique represents an option reducing geometric distortions and providing higher spatial resolution, however, with prolonged examination duration.
- Klíčová slova
- technika RESOLVE,
- MeSH
- difuzní magnetická rezonance * metody MeSH
- echoplanární zobrazování metody MeSH
- lidé MeSH
- tkáně MeSH
- vylepšení obrazu metody MeSH
- zobrazování difuzních tenzorů * metody MeSH
- zobrazování trojrozměrné MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
Although the field of sleep study has greatly developed over recent years, the most common and efficient way to detect sleep issues remains a sleep examination performed in a sleep laboratory. This examination measures several vital signals by polysomnograph during a full night's sleep using multiple sensors connected to the patient's body. Nevertheless, despite being the gold standard, the sensors and the unfamiliar environment's connection inevitably impact the quality of the patient's sleep and the examination itself. Therefore, with the novel development of accurate and affordable 3D sensing devices, new approaches for non-contact sleep study have emerged. These methods utilize different techniques to extract the same breathing parameters but with contactless methods. However, to enable reliable remote extraction, these methods require accurate identification of the basic region of interest (ROI), i.e., the patient's chest area. The lack of automated ROI segmenting of 3D time series is currently holding back the development process. We propose an automatic chest area segmentation algorithm that given a time series of 3D frames containing a sleeping patient as input outputs a segmentation image with the pixels that correspond to the chest area. Beyond significantly speeding up the development process of the non-contact methods, accurate automatic segmentation can enable a more precise feature extraction. In addition, further tests of the algorithm on existing data demonstrate its ability to improve the sensitivity of a prior solution that uses manual ROI selection. The approach is on average 46.9% more sensitive with a maximal improvement of 220% when compared to manual ROI. All mentioned can pave the way for placing non-contact algorithms as leading candidates to replace existing traditional methods used today.
- MeSH
- algoritmy * MeSH
- dýchání MeSH
- lidé MeSH
- počítačové zpracování obrazu metody MeSH
- polysomnografie MeSH
- spánek MeSH
- zobrazování trojrozměrné * metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články 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
BACKGROUND: Segmentation of pre-operative low-grade gliomas (LGGs) from magnetic resonance imaging is a crucial step for studying imaging biomarkers. However, segmentation of LGGs is particularly challenging because they rarely enhance after gadolinium administration. Like other gliomas, they have irregular tumor shape, heterogeneous composition, ill-defined tumor boundaries, and limited number of image types. To overcome these challenges we propose a semi-automated segmentation method that relies only on T2-weighted (T2W) and optionally post-contrast T1-weighted (T1W) images. METHODS: First, the user draws a region-of-interest (ROI) that completely encloses the tumor and some normal tissue. Second, a normal brain atlas and post-contrast T1W images are registered to T2W images. Third, the posterior probability of each pixel/voxel belonging to normal and abnormal tissues is calculated based on information derived from the atlas and ROI. Finally, geodesic active contours use the probability map of the tumor to shrink the ROI until optimal tumor boundaries are found. This method was validated against the true segmentation (TS) of 30 LGG patients for both 2D (1 slice) and 3D. The TS was obtained from manual segmentations of three experts using the Simultaneous Truth and Performance Level Estimation (STAPLE) software. Dice and Jaccard indices and other descriptive statistics were computed for the proposed method, as well as the experts' segmentation versus the TS. We also tested the method with the BraTS datasets, which supply expert segmentations. RESULTS AND DISCUSSION: For 2D segmentation vs. TS, the mean Dice index was 0.90 ± 0.06 (standard deviation), sensitivity was 0.92, and specificity was 0.99. For 3D segmentation vs. TS, the mean Dice index was 0.89 ± 0.06, sensitivity was 0.91, and specificity was 0.99. The automated results are comparable with the experts' manual segmentation results. CONCLUSIONS: We present an accurate, robust, efficient, and reproducible segmentation method for pre-operative LGGs.
- MeSH
- algoritmy MeSH
- gliom patologie chirurgie MeSH
- lidé MeSH
- magnetická rezonanční tomografie * metody MeSH
- nádory mozku patologie chirurgie MeSH
- počítačové zpracování obrazu * MeSH
- senzitivita a specificita MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
