scholarly journals The diagnostic performance of leak-plugging automated segmentation versus manual tracing of breast lesions on ultrasound images

Ultrasound ◽  
2017 ◽  
Vol 25 (2) ◽  
pp. 98-106 ◽  
Author(s):  
Hui Xiong ◽  
Laith R Sultan ◽  
Theodore W Cary ◽  
Susan M Schultz ◽  
Ghizlane Bouzghar ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Roc Curves ◽  
Lesion Size ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Breast Masses ◽  
Segmentation Methods ◽  
Malignant Lesions ◽  
Overlap Area ◽  
Better Than

Purpose To assess the diagnostic performance of a leak-plugging segmentation method that we have developed for delineating breast masses on ultrasound images. Materials and methods Fifty-two biopsy-proven breast lesion images were analyzed by three observers using the leak-plugging and manual segmentation methods. From each segmentation method, grayscale and morphological features were extracted and classified as malignant or benign by logistic regression analysis. The performance of leak-plugging and manual segmentations was compared by: size of the lesion, overlap area ( Oa) between the margins, and area under the ROC curves ( Az). Results The lesion size from leak-plugging segmentation correlated closely with that from manual tracing ( R2 of 0.91). Oa was higher for leak plugging, 0.92 ± 0.01 and 0.86 ± 0.06 for benign and malignant masses, respectively, compared to 0.80 ± 0.04 and 0.73 ± 0.02 for manual tracings. Overall Oa between leak-plugging and manual segmentations was 0.79 ± 0.14 for benign and 0.73 ± 0.14 for malignant lesions. Az for leak plugging was consistently higher (0.910 ± 0.003) compared to 0.888 ± 0.012 for manual tracings. The coefficient of variation of Az between three observers was 0.29% for leak plugging compared to 1.3% for manual tracings. Conclusion The diagnostic performance, size measurements, and observer variability for automated leak-plugging segmentations were either comparable to or better than those of manual tracings.

Research on Threshold Segmentation Algorithms

2013 ◽  
Vol 860-863 ◽  
pp. 2888-2891
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Ying Sun
Keyword(s):  
Pattern Recognition ◽  
Image Segmentation ◽  
Segmentation Method ◽  
Threshold Segmentation ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Better Than ◽  
Iterative Threshold

Thresholding is one of the critical steps in pattern recognition and has a significant effect on the upcoming steps of image application, the important objectives of thresholding are as follows, and separating objects from background, decreasing the capacity of data consequently increases speed. Various threshold segmentation methods are studied. These methods are compared by using MATLAB7.0. The qualities of image segmentation are elaborated. The results show that iterative threshold segmentation method is better than others.

scholarly journals Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation

2021 ◽  
Vol 45 (1) ◽  
pp. 122-129
Author(s):  
Dang N.H. Thanh ◽  
Nguyen Hoang Hai ◽  
Le Minh Hieu ◽  
Prayag Tiwari ◽  
V.B. Surya Prasath
Keyword(s):  
Skin Cancer ◽  
Skin Lesion ◽  
Semantic Segmentation ◽  
Segmentation Method ◽  
Lesion Segmentation ◽  
Computing Systems ◽  
Segmentation Methods ◽  
Dermoscopic Image ◽  
Better Than

Melanoma skin cancer is one of the most dangerous forms of skin cancer because it grows fast and causes most of the skin cancer deaths. Hence, early detection is a very important task to treat melanoma. In this article, we propose a skin lesion segmentation method for dermoscopic images based on the U-Net architecture with VGG-16 encoder and the semantic segmentation. Base on the segmented skin lesion, diagnostic imaging systems can evaluate skin lesion features to classify them. The proposed method requires fewer resources for training, and it is suitable for computing systems without powerful GPUs, but the training accuracy is still high enough (above 95 %). In the experiments, we train the model on the ISIC dataset – a common dermoscopic image dataset. To assess the performance of the proposed skin lesion segmentation method, we evaluate the Sorensen-Dice and the Jaccard scores and compare to other deep learning-based skin lesion segmentation methods. Experimental results showed that skin lesion segmentation quality of the proposed method are better than ones of the compared methods.

scholarly journals A Curvature-Enhanced Random Walker Segmentation Method for Detailed Capture of 3D Cell Surface Membranes

2019 ◽  
Author(s):  
E. Josiah Lutton ◽  
Sharon Collier ◽  
Till Bretschneider
Keyword(s):  
Cell Surface ◽  
Large Time ◽  
Segmentation Method ◽  
New Techniques ◽  
3D Images ◽  
Random Walker ◽  
Seeding Method ◽  
Segmentation Methods ◽  
Microscopy Data ◽  
Better Than

AbstractHigh-resolution 3D microscopy is a fast advancing field and requires new techniques in image analysis to handle these new datasets. In this work, we focus on detailed 3D segmentation of Dictyostelium cells undergoing macropinocytosis captured on an iSPIM microscope. We propose a novel random walker-based method with a curvature-based enhancement term, with the aim of capturing fine protrusions, such as filopodia and deep invaginations, such as macropinocytotic cups, on the cell surface. We tested our method on both real and synthetic 3D image volumes, demonstrating that the inclusion of the curvature enhancement term can improve the segmentation of the aforementioned features. We show that our method performs better than other state of the art segmentation methods in 3D images of Dictyostelium cells, and performs competitively against CNN-based methods in challenge datasets, demonstrating the ability to obtain accurate segmentations without the requirement of large training datasets. We also present an automated seeding method for microscopy data, which, combined with the curvature-enhanced random walker method, enables the segmentation of large time series with minimal input from the experimenter.

scholarly journals Qualitative and quantitative strain and shear wave elastography paradigm in differentiation of breast lesions

2022 ◽  
Vol 53 (1) ◽  
Author(s):  
Roaa M. A. Shehata ◽  
Mostafa A. M. El-Sharkawy ◽  
Omar M. Mahmoud ◽  
Hosam M. Kamel
Keyword(s):  
Shear Wave ◽  
Diagnostic Performance ◽  
Strain Ratio ◽  
Breast Lesions ◽  
Breast Masses ◽  
Significant Difference ◽  
Malignant Breast ◽  
Malignant Lesions ◽  
The Mean ◽  
Sensitivity Specificity

Abstract Background Breast cancer is the most common life-threatening cancer in women worldwide. A high number of women are going through biopsy procedures for characterization of breast masses every day and yet 75% of the pathological results prove these masses to be benign. Ultrasound (US) elastography is a non-invasive technique that measures tissue stiffness. It is convenient for differentiating benign from malignant breast tumors. Our study aims to evaluate the role of qualitative ultrasound elastography scoring (ES), quantitative mass strain ratio (SR), and shear wave elasticity ratio (SWER) in differentiation between benign and malignant breast lesions. Results Among 51 female patients with 77 histopathologically proved breast lesions, 57 breast masses were malignant and 20 were benign. All patients were examined by B-mode ultrasound then strain and shear wave elastographic examinations using ultrasound machine (Logiq E9, GE Medical Systems) with 8.5–12 MHz high-frequency probes. Our study showed that ES best cut-off point > 3 with sensitivity, specificity, PPV, NPP, accuracy was 94.7%, 85%, 94.7%, 85%, 90.9%, respectively, and AUC = 0.926 at P < 0.001, mass SR the best cut-off point > 4.6 with sensitivity, specificity, PPV, NPP, accuracy was 96.5%, 80%, 93.2%, 88.9%, 92.2%, respectively, and AUC = 0.860 at P < 0.001, SWER the best cut-off value > 4.9 with sensitivity, specificity, PPV, NPP and accuracy was 91.2%, 80%, 92.9%, 76.2%, 93.5%, respectively, and AUC = 0.890 at P < 0.001. The mean mass strain ratio for malignant lesions is 10.1 ± 3.7 SD and for solid benign lesions 4.7 ± 4.3 SD (p value 0.001). The mean shear wave elasticity ratio for malignant lesions is 10.6 ± 5.4 SD and for benign (solid and cystic) lesions 3.6 ± 4.2 SD. Using ROC curve and Youden index, the difference in diagnostic performance between ES, SR and SWER was not significant in differentiation between benign and malignant breast lesions and also was non-significant difference when comparing them with conventional US alone. Conclusion ES, SR, and SWER have a high diagnostic performance in differentiating malignant from benign breast lesions with no statistically significant difference between them.

Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer

2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar
Keyword(s):  
Human Lung ◽  
Research Work ◽  
Vital Role ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Background Segmentation ◽  
Lung Mri

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

Clinical Application of Computer-Aided Diagnosis According to S-Detect in Category 4 Breast Lesions: Prospective Single-Center Study (Preprint)

2020 ◽  
Author(s):  
Pengfei Sun ◽  
Chen Chen ◽  
Weiqi Wang ◽  
Lei Liang ◽  
Dan Luo ◽  
...  
Keyword(s):  
Clinical Management ◽  
Diagnostic Performance ◽  
Breast Biopsy ◽  
Computer Aided Diagnosis ◽  
Breast Lesions ◽  
Breast Masses ◽  
Computer Aided ◽  
Conventional Ultrasound ◽  
Us Findings ◽  
Aided Diagnosis

BACKGROUND Computer-aided diagnosis (CAD) is a useful tool that can provide a reference for the differential diagnosis of benign and malignant breast lesion. Previous studies have demonstrated that CAD can improve the diagnostic performance. However, conventional ultrasound (US) combined with CAD were used to adjust the classification of category 4 lesions has been few assessed. OBJECTIVE The objective of our study was to evaluate the diagnosis performance of conventional ultrasound combined with a CAD system S-Detect in the category of BI-RADS 4 breast lesions. METHODS Between December 2018 and May 2020, we enrolled patients in this study who received conventional ultrasound and S-Detect before US-guided biopsy or surgical excision. The diagnostic performance was compared between US findings only and the combined use of US findings with S-Detect, which were correlated with pathology results. RESULTS A total of 98 patients (mean age 51.06 ±16.25 years, range 22-81) with 110 breast masses (mean size1.97±1.38cm, range0.6-8.5) were included in this study. Of the 110 breast masses, 64/110 (58.18%) were benign, 46/110 (41.82%) were malignant. Compared with conventional ultrasound, a significant increase in specificity (0% to 53.12%, P<.001), accuracy (41.81% to70.19%, P<.001) were noted, with no statistically significant decrease on sensitivity(100% to 95.65% ,P=.48). According to S-Detect-guided US BI-RADS re-classification, 30 out of 110 (27.27%) breast lesions underwent a correct change in clinical management, 74of 110 (67.27%) breast lesions underwent no change and 6 of 110 (5.45%) breast lesions underwent an incorrect change in clinical management. The biopsy rate decreased from 100% to 67.27 % (P<.001).Benign masses among subcategory 4a had higher rates of possibly benign assessment on S-Detect for the US only (60% to 0%, P<.001). CONCLUSIONS S-Detect can be used as an additional diagnostic tool to improve the specificity and accuracy in clinical practice. S-Detect have the potential to be used in downgrading benign masses misclassified as BI-RADS category 4 on US by radiologist, and may reduce unnecessary breast biopsy. CLINICALTRIAL none

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

Automatic superpixel-based segmentation method for breast ultrasound images

2019 ◽  
Vol 121 ◽  
pp. 78-96 ◽  
Author(s):  
Mohammad I. Daoud ◽  
Ayman A. Atallah ◽  
Falah Awwad ◽  
Mahasen Al-Najjar ◽  
Rami Alazrai
Keyword(s):  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Segmentation Method

scholarly journals Assessment of Referrals into the Soft Tissue Sarcoma Service: Evaluation of Imaging Early in the Pathway Process

Sarcoma ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Emma Rowbotham ◽  
Shaheel Bhuva ◽  
Harun Gupta ◽  
Philip Robinson
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Service Evaluation ◽  
Lesion Size ◽  
Imaging Diagnosis ◽  
Local Hospital ◽  
Patient Demographics ◽  
Tertiary Centre ◽  
Soft Tissue Masses ◽  
Malignant Lesions

Purpose. To prospectively evaluate regional referrals into a soft tissue sarcoma service from outside the tertiary centre with local hospital imaging.Materials and Methods. Consecutive referrals were prospectively assessed for: patient demographics, source, referral date, date received by Multidisciplinary Team (MDT), lesion size, local radiology, MDT radiology and final diagnoses. Radiology diagnosis was categorised benign, indeterminate or malignant by consensus. Delays were defined as >10 days.Results. 112 patients were included with high correlation between local and MDT radiology categrorisation and histology (P=0.54andP=0.49, resp.). There was only a trend for MDT radiology diagnosis to downgrade local imaging diagnosis (n=15,P>0.05). 48 cases (43%) had ultrasound and MRI at referral and 20 (18%) ultrasound only. 85% of cases were benign (lipoma most common), 15% malignant (sarcoma most common). Delay occurred in 34% of cases.Discussion. In comparison to previous series these results show a reduction in benign lesions, increased biopsy and malignancy rate for lesions referred to a tertiary centre when imaging is performed and reviewed by local radiologists.Advances in Knowledge. Imaging triage of soft tissue masses can decrease benign referral rates and increase the proportion of indeterminate and malignant lesions referred to specialist centres.

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