scholarly journals Automatic Approach for Lung Segmentation with Juxta-Pleural Nodules from Thoracic CT Based on Contour Tracing and Correction

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Jinke Wang ◽  
Haoyan Guo
Keyword(s):  
Maximum Cost ◽  
Lung Segmentation ◽  
Surface Distance ◽  
Contour Segmentation ◽  
Promising Tool ◽  
Strong Focus ◽  
Pulmonary Parenchyma ◽  
Left And Right ◽  
Mean Square Distance ◽  
Volume Difference

This paper presents a fully automatic framework for lung segmentation, in which juxta-pleural nodule problem is brought into strong focus. The proposed scheme consists of three phases: skin boundary detection, rough segmentation of lung contour, and pulmonary parenchyma refinement. Firstly, chest skin boundary is extracted through image aligning, morphology operation, and connective region analysis. Secondly, diagonal-based border tracing is implemented for lung contour segmentation, with maximum cost path algorithm used for separating the left and right lungs. Finally, by arc-based border smoothing and concave-based border correction, the refined pulmonary parenchyma is obtained. The proposed scheme is evaluated on 45 volumes of chest scans, with volume difference (VD) 11.15±69.63 cm3, volume overlap error (VOE) 3.5057±1.3719%, average surface distance (ASD) 0.7917±0.2741 mm, root mean square distance (RMSD) 1.6957±0.6568 mm, maximum symmetric absolute surface distance (MSD) 21.3430±8.1743 mm, and average time-cost 2 seconds per image. The preliminary results on accuracy and complexity prove that our scheme is a promising tool for lung segmentation with juxta-pleural nodules.

scholarly journals Implants versus Lipograft – Analysis of Long-Term Results after Congenital Breast Asymmetry Procedure

2021 ◽  
Author(s):  
Vanessa Brebant ◽  
Maximilian Weiherer ◽  
Vivien Noisser ◽  
Stephan Seitz ◽  
Lukas Prantl ◽  
...  
Keyword(s):  
Breast Augmentation ◽  
Young Group ◽  
Long Term Results ◽  
Silicone Implants ◽  
Scanning System ◽  
Breast Asymmetry ◽  
Breast Symmetry ◽  
Left And Right ◽  
Volume Difference

Congenital breast asymmetry represents a particular challenge to the classical techniques of plastic surgery due to a young group of patients. This study compares traditional breast augmentation using silicone implants to the more innovative lipograft technique regarding long-term results. To achieve this, we not only captured subjective parameters like satisfaction with outcome and symmetry, but also objective parameters such as breast volume and anthropometric measurements. Objective examination was performed manually and by using the Vectra® H2 photogrammetry scanning system. Patients who underwent implant augmentation and lipograft both showed no significant differences in patient´s satisfaction with surgical outcome (p = 0.55) and symmetry (p = 0.69). Furthermore, a breast symmetry of 93 % in both groups was reported. Likewise, no statistically significant volume difference between left and right breast was observed in both groups (p<0.41). However, on average, lipograft patients needed 1.3 procedures more until the desired result was achieved. In contrast, patients treated with implant-based breast augmentation usually need several implant changes during their life. In conclusion, both methods should be considered for patients with congenital breast asymmetry.

scholarly journals 68Ga-DOTA-FAPI-04 PET/CT as a Promising Tool for Differentiating Ovarian Physiological Uptake: Preliminary Experience of Comparative Analysis With 18F-FDG

2021 ◽  
Vol 8 ◽  
Author(s):  
Qixin Wang ◽  
Songsong Yang ◽  
Wenxin Tang ◽  
Lin Liu ◽  
Yue Chen
Keyword(s):  
Ct Scanning ◽  
Pathological Changes ◽  
Physiological Uptake ◽  
Medicine Department ◽  
Promising Tool ◽  
Pet Ct ◽  
Significant Difference ◽  
Ovarian Diseases ◽  
Left And Right

Objectives: This study aimed to investigate the physiological distribution characteristics of 68Ga-DOTA-FAPI-04 in the ovary, and assess the feasibility of early diagnosis of primary ovarian disease with 68Ga-DOTA-FAPI-04 PET/CT.Methods: We retrospectively analyzed the data of patients who received 18F-FDG and 68Ga-DOTA-FAPI-04 PET/CT scanning in the Nuclear Medicine Department of our hospital within 3 days from September 2020 to January 2021. We selected the data in which ovaries showed abnormal FDG activity. Patients with abnormal ovarian FDG uptake with focus confirmed by pathological biopsy or clinical follow-up as pathological changes were excluded. The uptake of tracers (18F-FDG and 68Ga-FAPI) was semi-quantitatively analyzed.Results: This study included 14 patients (average age was 38.6). Physiological ovarian uptake was mostly unilateral, and there was no significant difference in SUVmax between the left and right sides (FDGt = 0.272, FAPIt = 0.592). The ovary SUVmax of FDG (4.89 ± 1.84) was statistically significantly higher than that of FAPI (1.53 ± 0.37). The Le/Li ratio on FDG is 3.38 ± 1.81, TBR is 5.81 ± 1.98, while the Le/Li ratio on FAPI is 3.57 ± 1.26, TBR is 0.94 ± 0.19.Conclusion: Our research shows that ovarian functional or pathological changes can be manifested as FDG avid, while 68Ga-DOTA-FAPI-04 has no physiological accumulation in the ovary and is not affected by the menstrual cycle. Therefore, 68Ga-DOTA-FAPI-04 has unique advantages in the diagnosis of ovarian diseases, and can identify them early and accurately.

scholarly journals Improvement of Classification and Clustering of Images using Evolutionary Techniques

2019 ◽  
Vol 8 (9) ◽  
pp. 1700-1703
Keyword(s):  
Medical Image ◽  
Region Of Interest ◽  
Body Part ◽  
Relative Volume ◽  
Medical Image Segmentation ◽  
The Body ◽  
Performance Factors ◽  
Surface Distance ◽  
Symmetric Surface ◽  
Volume Difference

Segmentation of medical images is significant as it aids in mining the region of interest, such that the body part under analysis is extracted. Medical image segmentation helps in treatment of diseases, in surgeries and also aids in medical diagnosis. Various performance factors like Volumetric Overlap Error, Relative Volume Difference, Average Symmetric Surface Distance, Root Mean Square Symmetric Surface Distance, Maximum Symmetric Surface Distance. were evaluated which shows that outlier detection technique provides better results as compared to the implementation done without using this technique

scholarly journals Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis

2021 ◽  
Author(s):  
Young-Gon Kim ◽  
Kyungsang Kim ◽  
Dufan Wu ◽  
Hui Ren ◽  
Won Young Tak ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chest Radiography ◽  
Quantitative Description ◽  
Semantic Interpretation ◽  
Lung Edema ◽  
Lung Segmentation ◽  
Ensemble Strategy ◽  
Left And Right ◽  
Accurate Quantification

Abstract Imaging plays an important role in assessing severity of COVID-19 pneumonia. The recent COVID-19 researches indicate that in many cases, disease progress propagates from the bottom of the lungs to the top. However, semantic interpretation of chest radiography (CXR) findings do not provide a quantitative description of radiographic opacities, and the existing AI-assisted CXR image analysis frameworks do not quantify the severity regionally. To address this issue, we propose a deep learning-based four-region lung segmentation method to assist accurate quantification of COVID-19 pneumonia. Specifically, a segmentation model to separate left and right lung is firstly applied, and then a carina and left hilum detection network is used to separate the upper and lower lungs. To improve the segmentation performance of COVID-19 images, ensemble strategy with five models is exploited. For each region, we evaluated the clinical relevance of the proposed method compared with the Radiographic Assessment of the Quality of Lung Edema (RALE). The proposed ensemble strategy showed dice score of 0.900, which outperforms the conventional methods. Mean intensities of segmented four regions indicate positive correlation to the extent and density scores of pulmonary opacities based on the RALE framework. Therefore, the proposed method can accurately segment four-regions of the lungs and quantify regional pulmonary opacities of COVID-19 pneumonia patients.

scholarly journals Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 101
Author(s):  
Young-Gon Kim ◽  
Kyungsang Kim ◽  
Dufan Wu ◽  
Hui Ren ◽  
Won Young Tak ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chest Radiography ◽  
Clinical Relevance ◽  
Regional Analysis ◽  
Lung Edema ◽  
Radiographic Assessment ◽  
Lung Segmentation ◽  
Ensemble Strategy ◽  
Left And Right

Imaging plays an important role in assessing the severity of COVID-19 pneumonia. Recent COVID-19 research indicates that the disease progress propagates from the bottom of the lungs to the top. However, chest radiography (CXR) cannot directly provide a quantitative metric of radiographic opacities, and existing AI-assisted CXR analysis methods do not quantify the regional severity. In this paper, to assist the regional analysis, we developed a fully automated framework using deep learning-based four-region segmentation and detection models to assist the quantification of COVID-19 pneumonia. Specifically, a segmentation model is first applied to separate left and right lungs, and then a detection network of the carina and left hilum is used to separate upper and lower lungs. To improve the segmentation performance, an ensemble strategy with five models is exploited. We evaluated the clinical relevance of the proposed method compared with the radiographic assessment of the quality of lung edema (RALE) annotated by physicians. Mean intensities of segmented four regions indicate a positive correlation to the regional extent and density scores of pulmonary opacities based on the RALE. Therefore, the proposed method can accurately assist the quantification of regional pulmonary opacities of COVID-19 pneumonia patients.

scholarly journals Automatic Lung Segmentation Algorithm on Chest X-ray Images Based on Fusion Variational Auto-Encoder and Three-Terminal Attention Mechanism

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 814
Author(s):  
Feidao Cao ◽  
Huaici Zhao
Keyword(s):  
Automatic Segmentation ◽  
Attention Mechanism ◽  
Segmentation Algorithm ◽  
Target Area ◽  
Lung Segmentation ◽  
X Ray ◽  
Chest X Ray ◽  
Left And Right ◽  
High Level ◽  
Better Than

Automatic segmentation of the lungs in Chest X-ray images (CXRs) is a key step in the screening and diagnosis of related diseases. There are many opacities in the lungs in the CXRs of patients, which makes the lungs difficult to segment. In order to solve this problem, this paper proposes a segmentation algorithm based on U-Net. This article introduces variational auto-encoder (VAE) in each layer of the decoder-encoder. VAE can extract high-level semantic information, such as the symmetrical relationship between the left and right thoraxes in most cases. The fusion of the features of VAE and the features of convolution can improve the ability of the network to extract features. This paper proposes a three-terminal attention mechanism. The attention mechanism uses the channel and spatial attention module to automatically highlight the target area and improve the performance of lung segmentation. At the same time, the three-terminal attention mechanism uses the advanced semantics of high-scale features to improve the positioning and recognition capabilities of the attention mechanism, suppress background noise, and highlight target features. Experimental results on two different datasets show that the accuracy (ACC), recall (R), F1-Score and Jaccard values of the algorithm proposed in this paper are the highest on the two datasets, indicating that the algorithm in this paper is better than other state-of-the-art algorithms.

scholarly journals Great potential of ultrasound elastography for the assessment of the masseter muscle in patients with temporomandibular disorders. A systematic review

2020 ◽  
Vol 49 (8) ◽  
pp. 20200024 ◽  
Author(s):  
Anna Olchowy ◽  
Mieszko Wieckiewicz ◽  
Efraim Winocur ◽  
Marzena Dominiak ◽  
Ilona Dekkers ◽  
...  
Keyword(s):  
Temporomandibular Disorders ◽  
Masseter Muscle ◽  
High Reliability ◽  
Ultrasound Elastography ◽  
Muscle Disorders ◽  
Promising Tool ◽  
Study Protocols ◽  
Left And Right ◽  
Phantom Studies ◽  
Meta Analyses

Objective: To summarize the available evidence on the use of elastography in the assessment of the masseter muscle in healthy individuals and patients with masseter muscle disorders. Methods: Systematic literature review has been performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Results: 16 of 142 studies identified were analyzed. Elastography was used in seven studies. Heterogeneity was observed in terms of study protocols, devices, patients, units of measure, and results. Elasticity values showed a correlation between the left and right masseter muscle side in healthy people, but not in patients with temporomandibular disorders (TMDs). Elasticity values increased in TMD and were correlated with the severity of TMD symptoms. Phantom studies proved the high reliability of elastography. Conclusion: Elastography is a promising tool for the assessment of the masseter muscle elasticity, but the evidence is insufficient. Studies on larger groups are needed to determine the accuracy of elastography to characterize masticatory muscle disorders.

scholarly journals CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarah E. Gerard ◽  
Jacob Herrmann ◽  
Yi Xin ◽  
Kevin T. Martin ◽  
Emanuele Rezoagli ◽  
...  
Keyword(s):  
Neural Network ◽  
Hierarchical Clustering ◽  
Segmentation Algorithm ◽  
Training Data ◽  
Lung Segmentation ◽  
Computed Tomography Images ◽  
Training Approach ◽  
Lung Abnormalities ◽  
Left And Right ◽  
Symmetric Surface

AbstractThe purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of $$0.495\pm 0.309$$ 0.495 ± 0.309 mm and Dice coefficient of $$0.985\pm 0.011$$ 0.985 ± 0.011 . Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.

The three-dimensional structure of frozen-hydrated left- and right-handed forms of bacterial flagellar filaments from an identical serotype

1986 ◽  
Vol 44 ◽  
pp. 298-299
Author(s):  
S. Trachtenberg ◽  
D. J. DeRosier
Keyword(s):  
Ionic Strength ◽  
Basal Body ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Protein Species ◽  
Liquid Environment ◽  
Bacterial Flagellum ◽  
Left And Right ◽  
Rotary Motor ◽  
Helical Parameters

The bacterial cell is propelled through the liquid environment by means of one or more rotating flagella. The bacterial flagellum is composed of a basal body (rotary motor), hook (universal coupler), and filament (propellor). The filament is a rigid helical assembly of only one protein species — flagellin. The filament can adopt different morphologies and change, reversibly, its helical parameters (pitch and hand) as a function of mechanical stress and chemical changes (pH, ionic strength) in the environment.

Advantages of Complementary Stereo Images as Viewed with the Low-Temperature Field-Emission SEM

1992 ◽  
Vol 50 (2) ◽  
pp. 1314-1315
Author(s):  
William P. Wergin ◽  
Eric F. Erbe
Keyword(s):  
Fold Increase ◽  
Horizontal Displacement ◽  
Three Dimensions ◽  
Stereo Image ◽  
Stereo Pair ◽  
Sem Micrograph ◽  
Left And Right ◽  
The Common ◽  
The Brain ◽  
Pair Analysis

The eye-brain complex allows those of us with normal vision to perceive and evaluate our surroundings in three-dimensions (3-D). The principle factor that makes this possible is parallax - the horizontal displacement of objects that results from the independent views that the left and right eyes detect and simultaneously transmit to the brain for superimposition. The common SEM micrograph is a 2-D representation of a 3-D specimen. Depriving the brain of the 3-D view can lead to erroneous conclusions about the relative sizes, positions and convergence of structures within a specimen. In addition, Walter has suggested that the stereo image contains information equivalent to a two-fold increase in magnification over that found in a 2-D image. Because of these factors, stereo pair analysis should be routinely employed when studying specimens.Imaging complementary faces of a fractured specimen is a second method by which the topography of a specimen can be more accurately evaluated.

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