regional segmentation
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... Infratemporal and Submandibular -- Regions. Second Stage. 78. ... ... Infratemporal and Submandibular -- Regions. Third Stage. 79. ... ... Anterior Median Region of Neck. 104. Prevertebral Region. 105a. Cartilages of Larynx. ?. ... ... Scapular Region, from behind. 243. Scapular Region, from in front. 244. ... ... Cutaneous Nerves of Gluteal Region and -- Back of Thigh. 285. Gluteal Region I. 286. ...
6th ed. xii, 320, 16 s. : il., tab.
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
BACKGROUND: Because of its non-destructive nature, label-free imaging is an important strategy for studying biological processes. However, routine microscopic techniques like phase contrast or DIC suffer from shadow-cast artifacts making automatic segmentation challenging. The aim of this study was to compare the segmentation efficacy of published steps of segmentation work-flow (image reconstruction, foreground segmentation, cell detection (seed-point extraction) and cell (instance) segmentation) on a dataset of the same cells from multiple contrast microscopic modalities. RESULTS: We built a collection of routines aimed at image segmentation of viable adherent cells grown on the culture dish acquired by phase contrast, differential interference contrast, Hoffman modulation contrast and quantitative phase imaging, and we performed a comprehensive comparison of available segmentation methods applicable for label-free data. We demonstrated that it is crucial to perform the image reconstruction step, enabling the use of segmentation methods originally not applicable on label-free images. Further we compared foreground segmentation methods (thresholding, feature-extraction, level-set, graph-cut, learning-based), seed-point extraction methods (Laplacian of Gaussians, radial symmetry and distance transform, iterative radial voting, maximally stable extremal region and learning-based) and single cell segmentation methods. We validated suitable set of methods for each microscopy modality and published them online. CONCLUSIONS: We demonstrate that image reconstruction step allows the use of segmentation methods not originally intended for label-free imaging. In addition to the comprehensive comparison of methods, raw and reconstructed annotated data and Matlab codes are provided.
- MeSH
- algoritmy MeSH
- frakcionace buněk metody MeSH
- lidé MeSH
- mikroskopie metody MeSH
- počítačové zpracování obrazu MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Cíl studie: Cílem retrospektivní studie bylo srovnat hemodynamický stav pacientů a četnost perioperačních oběhových komplikací při operacích krčních tepen ve dvou typech regionální anestezie, a to v bloku cervikálního plexu a krční epidurální anestezii. Typ studie: Retrospektivní klinická studie. Název a sídlo pracoviště: Úsek kardiovaskulární anestezie a intenzivní péče, Nemocnice Na Homolce, Praha; Klinika transplantační chirurgie, Institut klinické a experimentální medicíny, Praha. Materiál a metodika: Do retrospektivní klinické studie byli zařazeni 493 pacienti, kteří v období 1998–2003 podstoupili 529 endarterektomií vnitřní krkavice v regionální anestezii. Výkon v bloku cervikálního plexu (CPB) podstoupilo 313 pacientů, v krční epidurální anestezii (CEA) jich bylo 216. Byly zaznamenávány tyto parametry: hodnoty systolického, diastolického a středního arteriálního tlaku, srdeční frekvence a případné poruchy rytmu, detekovány změny úseku ST na EKG, výskyt stenokardií, akutní ischémiemyokardu v perioperačním období a celková mortalita z kardiálních příčin. Data byla statisticky zpracována pomocí χ2 testu dobré shody. Výsledky: Hypertenzi s nutností farmakoterapie jsme zaznamenali u 181 pacientů ve skupině CPB (57,8 %) a u 25 nemocných ve skupiněCEA (11,6 %);p < 0,001. Hypotenze a/nebo bradykardie byly přítomny u 30 pacientů ve skupině CPB (9,6 %) a u 41 nemocných ve skupině CEA; (18,9 %). Závažné poruchy srdečního rytmu jsme zaznamenali u 16 pacientů (3,0 %) – 14krát u CPB, 2krát u CEA; p < 0,05. Perioperační průběh byl komplikován akutním infarktem myokardu u 6 pacientů ve skupině CPB (2,0 %) a u 1 nemocného ve skupině CEA (0,5 %). Perioperační mortalita v souboru byla 6 nemocných (1,1 %, 5 ve skupině CPB a 1 ve skupině CEA). Příčinou úmrtí byla 4krát intraoperační cévní mozková příhoda, 1krát akutní infarkt myokardu s následným srdečním selháním a 1krát bronchopneumonie. Závěr: Obě metody regionální anestezie jsou vhodné k provedení endarterektomie a. carotis interna. Blokáda cervikálního plexu je spojena se zvýšeným výskytem kardiovaskulární nestability (hladina významnosti p < 0,05, resp. 0,001), především hypertenze a perioperační ischémie myokardu. U pacientů se závažnou kardiální anamnézou doporučujeme použití krční epidurální anestezie, především pro její blokádu stresové odpovědi, sympatické inervace srdce a koronarodilatační efekt.
Objective: The aim of this retrospective study was to evaluate a haemodynamic profile of the patients during carotid endarterectomy under regional anaesthesia technique and to compare the perioperative cardiovascular stability and complication rate of cervical plexus block (CPB) and cervical epidural anaesthesia (CEA). Design: Retrospective clinical study. Setting: Department of Cardiovascular Anaesthesia and Intensive Care, Na Homolce Hospital, Prague, Czech Republic, Department of Transplant Surgery, IKEM, Prague, Czech Republic. Material and Methods: A total of 529 carotid artery endarterectomies performed under regional anaesthesia in 1998–2003 period were included into this retrospective study. A total of 313 procedures were performed using cervical plexus block, while cervical epidural anaesthesia was used in 216 operations. The evaluated parameters included the systolic, diastolic and mean arterial pressures, heart rate before, during and after the procedure. The circulatory complications, including chest pain, ST segment depression and acute myocardial infarction in the perioperative period were also evaluated. The obtained data were analyzed statistically using chi-square test. Results: Hypertension requiring a pharmacological intervention was noted in 181 patients in a CPB group (57.8%) and in 25 patients in a CEA group (11.6%) respectively; P < 0.001. Hypotension and/or bradycardia were recorded in 30 patients in a CPB group (9.6%) and in 41 patients in a CEA group (18.9%). Life threatening dysrhytmias were noted in 16 cases (3.0%) (14 cases in a CPB group, 2 cases in a CEA group) (P < 0.05). An acute myocardial infarction complicated the perioperative course in 6 CPB/1 CEA (1.9% vs. 0.5%). Total intraoperative mortality in the cohort was 6 patients (1.1%, 5 CPB/1 CEA). 4 deaths were related to intraoperative stroke, 1 death (the patient in a CPB group) because of the myocardial infarction with subsequent heart failure, 1 patient died on a respiratory failure following to bronchopneumonia. Conclusion: Both techniques of regional anaesthesia are reliable for carotid artery surgery. A cervical plexus block is associated with higher rates of cardiovascular complications related mainly to hypertension and the risk of perioperativemyocardial ischemia.We recommend to use cervical epidural anaesthesia in patients with a serious cardiac history, mainly because of its ability to block the stress response and its coronary dilating effect.
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
The autofluorescence (AF) derived from the retinal pigment epithelium has the potential to be used as a feature for early glaucoma diagnosis. The aim of this paper is to design and evaluate the method used for semi-automatic segmentation of the zones with increased autofluorescence. A randomized sample of 20 patients (age: 56 +/- 10 years) with open angle glaucoma was used. A new method for semi-automatic detection and segmentation of the zones with a higher level of autofluorescence in the junctional zone of parapapillary atrophy has been evaluated. Good agreement has been observed between manually and semi-automatically segmented zones for most of the images, but higher differences may occur for larger hyperfluorescent regions. The data were evaluated using the limits of agreement, with a 2sigma border. The described method allows fast and objective evaluation of AF images, preventing undesirable inter-expert variance. A large proportion of AF images could be evaluated successfully by the developed procedure, and the obtained results are comparable to the manual procedure in most cases.
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.
Neuromelanin (NM) is a black pigment located in the brain in substantia nigra pars compacta (SN) and locus coeruleus. Its loss is directly connected to the loss of nerve cells in this part of the brain, which plays a role in Parkinson's Disease. Magnetic resonance imaging (MRI) is an ideal tool to monitor the amount of NM in the brain in vivo. The aim of the study was the development of tools and methodology for the quantification of NM in a special neuromelanin-sensitive MRI images. The first approach was done by creating regions of interest, corresponding to the anatomical position of SN based on an anatomical atlas and determining signal intensity threshold. By linking the anatomical and signal intensity information, we were able to segment the SN. As a second approach, the neural network U-Net was used for the segmentation of SN. Subsequently, the volume characterizing the amount of NM in the SN region was calculated. To verify the method and the assumptions, data available from various patient groups were correlated. The main benefit of this approach is the observer-independency of quantification and facilitation of the image processing process and subsequent quantification compared to the manual approach. It is ideal for automatic processing many image sets in one batch.
- MeSH
- deep learning * MeSH
- lidé středního věku MeSH
- lidé MeSH
- magnetická rezonanční tomografie metody MeSH
- melaniny analýza MeSH
- počítačové zpracování obrazu MeSH
- prodromální symptomy MeSH
- senioři MeSH
- studie případů a kontrol MeSH
- substantia nigra diagnostické zobrazování MeSH
- synukleinopatie diagnostické zobrazování MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- klinické zkoušky MeSH
