scholarly journals Recognition and Volume Measurement of Intracranial Hematoma through CT Images under Intelligent Recognition Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuequn Xu
Keyword(s):  
Level Set ◽  
Volume Measurement ◽  
Ct Images ◽  
Dice Similarity Coefficient ◽  
Hematoma Volume ◽  
Intracranial Hematoma ◽  
Data Set ◽  
Running Time ◽  
Fcm Algorithm ◽  
Fuzzy C Means

In order to realize the automatic recognition of intracranial hematoma and accurate measurement of hematoma volume in patients with ICH (intracerebral hemorrhage), the FCM algorithm (fuzzy c-means algorithm) was improved in this study, and a new level set segmentation algorithm based on FCM was obtained, FCRLS (fuzzy c-means regularized level set). Then, 120 ICH patients were used as the research objects, and the FCRLS algorithm was evaluated by the recall, precise, and F1-score values to evaluate the effect of intracranial hematoma recognition. The CT images of 48 patients with intracranial hematoma were used as the data set of the FCRLS algorithm. The hematoma was segmented, and the DSC (Dice similarity coefficient) value and running time were used to evaluate the segmentation results of the algorithm. At the same time, the LS (level set) algorithm and the FCM algorithm were introduced for comparison. The results show that the recall value of the FCRLS algorithm is 0.89, the precise value is 0.94, the F1-score value is 0.91, the Dice coefficient is 94.81%, and the running time is 14.48 s. Compared with the LS algorithm and the FCM algorithm, the above five indicators have significant differences ( P < 0.05 ). Hematoma volume measurement found that the average error of FCRLS algorithm from expert measurement results was 5.62%, which was statistically significant compared with LS algorithm and FCM algorithm ( P < 0.05 ). In summary, the FCRLS algorithm can accurately identify the cerebral hematoma area of ICH patients, has an ideal segmentation effect on the hematoma, and can accurately measure the true volume of the hematoma, which is worthy of clinical application.

A scalable eigenspace-based fuzzy c-means for topic detection

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hendri Murfi
Keyword(s):  
Representation Learning ◽  
Detection Methods ◽  
Data Sets ◽  
Topic Detection ◽  
Data Set ◽  
Content Type ◽  
Running Time ◽  
Fuzzy C Means ◽  
Coherence Score ◽  
Value Decomposition

PurposeThe aim of this research is to develop an eigenspace-based fuzzy c-means method for scalable topic detection.Design/methodology/approachThe eigenspace-based fuzzy c-means (EFCM) combines representation learning and clustering. The textual data are transformed into a lower-dimensional eigenspace using truncated singular value decomposition. Fuzzy c-means is performed on the eigenspace to identify the centroids of each cluster. The topics are provided by transforming back the centroids into the nonnegative subspace of the original space. In this paper, we extend the EFCM method for scalability by using the two approaches, i.e. single-pass and online. We call the developed topic detection methods as oEFCM and spEFCM.FindingsOur simulation shows that both oEFCM and spEFCM methods provide faster running times than EFCM for data sets that do not fit in memory. However, there is a decrease in the average coherence score. For both data sets that fit and do not fit into memory, the oEFCM method provides a tradeoff between running time and coherence score, which is better than spEFCM.Originality/valueThis research produces a scalable topic detection method. Besides this scalability capability, the developed method also provides a faster running time for the data set that fits in memory.

Assessment of the ABC/2 Method of Epidural Hematoma Volume Measurement as Compared to Computer-Assisted Planimetric Analysis

2015 ◽  
Vol 18 (1) ◽  
pp. 5-11 ◽  
Author(s):  
Ting-Ting Hu ◽  
Ling Yan ◽  
Peng-Fei Yan ◽  
Xuan Wang ◽  
Ge-Fen Yue
Keyword(s):  
Decision Making ◽  
Epidural Hematoma ◽  
Interobserver Reliability ◽  
Treatment Decision ◽  
Volume Measurement ◽  
Computer Assisted ◽  
Hematoma Volume ◽  
Data Set ◽  
Treatment Decision Making ◽  
The Difference

Background and Objective: Epidural hematoma volume (EDHV) is an independent predictor of prognosis in patients with epidural hematoma (EDH) and plays a central role in treatment decision making. This study’s objective was to determine the accuracy and reliability of the widely used volume measurement method ABC/2 in estimating EDHV by comparing it to the computer-assisted planimetric method. Methods: A data set of computerized tomography (CT) scans of 35 patients with EDH was evaluated to determine the accuracy of ABC/2 method, using computer-assisted planimetric technique to establish the reference criterion of EDHV for each patient. Another data set was constructed by randomly selecting 5 patients then replicating each case twice to yield 15 patients. Intra- and interobserver reliability were evaluated by asking four observers to independently estimate EDHV for the latter data set using the ABC/2 method. Results: Estimation of EDHV using the ABC/2 method showed high intra- and interobserver reliability (intra-class correlation coefficient = .99). These estimates were closely correlated with planimetric measures ( r = .99). But the ABC/2 method generally overestimated EDHV, especially in the nonellipsoid-like group. The difference between the ABC/2 measures and planimetric measures was statistically significant ( p < .05). Conclusions: The ABC/2 method could be used for EDHV measurement, which would contribute to treatment decision making as well as clinical outcome prediction. However, clinicians should be aware that the ABC/2 method results in a general volume overestimation. Future studies focusing on justification of the technique to improve its accuracy would be of practical value.

A precise, simple, convenient and new method for estimation of intracranial hematoma volume–the formula 2/3Sh

2009 ◽  
Vol 31 (10) ◽  
pp. 1031-1036 ◽  
Author(s):  
Kai-Jun Zhao ◽  
Yu Liu ◽  
Ru-Yuan Zhang ◽  
Xiao-Qiang Wang ◽  
Chengjin Gao ◽  
...  
Keyword(s):  
New Method ◽  
Hematoma Volume ◽  
Intracranial Hematoma

SEGMENTATION OF ABDOMEN DISEASES USING ACTIVE CONTOUR MODELS IN CT IMAGES

2015 ◽  
Vol 27 (05) ◽  
pp. 1550047 ◽  
Author(s):  
Gaurav Sethi ◽  
B. S. Saini
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Diagnostic System ◽  
Ct Images ◽  
Active Contour Models ◽  
Segmentation Methods ◽  
Contour Models ◽  
Edge Based ◽  
Level Set Evolution

Precise segmentation of abdomen diseases like tumor, cyst and stone are crucial in the design of a computer aided diagnostic system. The complexity of shapes and similarity of texture of disease with the surrounding tissues makes the segmentation of abdomen related diseases much more challenging. Thus, this paper is devoted to the segmentation of abdomen diseases using active contour models. The active contour models are formulated using the level-set method. Edge-based Distance Regularized Level Set Evolution (DRLSE) and region based Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) are used for segmentation of various abdomen diseases. These segmentation methods are applied on 60 CT images (20 images each of tumor, cyst and stone). Comparative analysis shows that edge-based active contour models are able to segment abdomen disease more accurately than region-based level set active contour model.

scholarly journals Application of Multi-Scale Fusion Attention U-Net to Segment the Thyroid Gland on CT Localization Images for Radiotherapy

2021 ◽  
Author(s):  
Xiaobo Wen ◽  
Biao Zhao ◽  
Meifang Yuan ◽  
Jinzhi Li ◽  
Mengzhen Sun ◽  
...  
Keyword(s):  
Thyroid Gland ◽  
Clinical Work ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Training Set ◽  
Data Set ◽  
Test Set ◽  
Noise Interference ◽  
Multi Scale ◽  
Validation Set

Abstract Objectives: To explore the performance of Multi-scale Fusion Attention U-net (MSFA-U-net) in thyroid gland segmentation on CT localization images for radiotherapy. Methods: CT localization images for radiotherapy of 80 patients with breast cancer or head and neck tumors were selected; label images were manually delineated by experienced radiologists. The data set was randomly divided into the training set (n=60), the validation set (n=10), and the test set (n=10). Data expansion was performed in the training set, and the performance of the MSFA-U-net model was evaluated using the evaluation indicators Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), positive predictive value (PPV), sensitivity (SE), and Hausdorff distance (HD). Results: With the MSFA-U-net model, the DSC, JSC, PPV, SE, and HD indexes of the segmented thyroid gland in the test set were 0.8967±0.0935, 0.8219±0.1115, 0.9065±0.0940, 0.8979±0.1104, and 2.3922±0.5423, respectively. Compared with U-net, HR-net, and Attention U-net, MSFA-U-net showed that DSC increased by 0.052, 0.0376, and 0.0346 respectively; JSC increased by 0.0569, 0.0805, and 0.0433, respectively; SE increased by 0.0361, 0.1091, and 0.0831, respectively; and HD increased by −0.208, −0.1952, and −0.0548, respectively. The test set image results showed that the thyroid edges segmented by the MSFA-U-net model were closer to the standard thyroid delineated by the experts, in comparison with those segmented by the other three models. Moreover, the edges were smoother, over-anti-noise interference was stronger, and oversegmentation and undersegmentation were reduced. Conclusion: The MSFA-U-net model can meet basic clinical requirements and improve the efficiency of physicians' clinical work.

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