scholarly journals Automatic Polyp Segmentation in Colonoscopy Images Using a Modified Deep Convolutional Encoder-Decoder Architecture

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5630
Author(s):  
Chin Yii Eu ◽  
Tong Boon Tang ◽  
Cheng-Hung Lin ◽  
Lok Hua Lee ◽  
Cheng-Kai Lu
Keyword(s):  
Colorectal Cancer ◽  
Automatic Segmentation ◽  
Hospital Setting ◽  
Decoder Architecture ◽  
The Future ◽  
Sensitivity Specificity ◽  
Convolutional Encoder ◽  
Cancer Côlon ◽  
Aided Diagnosis ◽  
Better Than

Colorectal cancer has become the third most commonly diagnosed form of cancer, and has the second highest fatality rate of cancers worldwide. Currently, optical colonoscopy is the preferred tool of choice for the diagnosis of polyps and to avert colorectal cancer. Colon screening is time-consuming and highly operator dependent. In view of this, a computer-aided diagnosis (CAD) method needs to be developed for the automatic segmentation of polyps in colonoscopy images. This paper proposes a modified SegNet Visual Geometry Group-19 (VGG-19), a form of convolutional neural network, as a CAD method for polyp segmentation. The modifications include skip connections, 5 × 5 convolutional filters, and the concatenation of four dilated convolutions applied in parallel form. The CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB databases were used to evaluate the model, and it was found that our proposed polyp segmentation model achieved an accuracy, sensitivity, specificity, precision, mean intersection over union, and dice coefficient of 96.06%, 94.55%, 97.56%, 97.48%, 92.3%, and 95.99%, respectively. These results indicate that our model performs as well as or better than previous schemes in the literature. We believe that this study will offer benefits in terms of the future development of CAD tools for polyp segmentation for colorectal cancer diagnosis and management. In the future, we intend to embed our proposed network into a medical capsule robot for practical usage and try it in a hospital setting with clinicians.

Pattern-Recognition: A Comparison of the Performance of Clinicians and Non-Clinicians - with a Note on the Performance of a Computer-Based System

1972 ◽  
Vol 11 (01) ◽  
pp. 32-37 ◽  
Author(s):  
F. T. DE DOMBAL ◽  
J. C. HORROCKS ◽  
J. R. STANILAND ◽  
P. J. GUILLOU
Keyword(s):  
Pattern Recognition ◽  
Computer System ◽  
Computer Aided Diagnosis ◽  
Clinical Interview ◽  
Computer Performance ◽  
Pattern Size ◽  
Computer Aided ◽  
Computer Based ◽  
Aided Diagnosis ◽  
Better Than

This paper describes a series of 10,500 attempts at »pattern-recognition« by two groups of humans and a computer based system. There was little difference between the performances of 11 clinicians and 11 other persons of comparable intellectual capability. Both groups’ performances were related to the pattern-size, the accuracy diminishing rapidly as the patterns grew larger. By contrast the computer system increased its accuracy as the patterns increased in size.It is suggested (a) that clinicians are very little better than others at pattem-recognition, (b) that the clinician is incapable of analysing on a probabilistic basis the data he collects during a traditional clinical interview and examination and (c) that the study emphasises once again a major difference between human and computer performance. The implications as - regards human- and computer-aided diagnosis are discussed.

scholarly journals Eotaxins and Their Receptor as Biomarkers of Colorectal Cancer

2021 ◽  
Vol 10 (12) ◽  
pp. 2675
Author(s):  
Monika Zajkowska ◽  
Agnieszka Kulczyńska-Przybik ◽  
Maciej Dulewicz ◽  
Kamil Safiejko ◽  
Marcin Juchimiuk ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Diagnostic Tests ◽  
Diagnostic Utility ◽  
Healthy Controls ◽  
Serum Concentrations ◽  
Chemiluminescent Microparticle Immunoassay ◽  
Sensitivity Specificity ◽  
Immunoenzymatic Assay

Colorectal cancer (CRC) is one of the most common malignancies. Despite the availability of diagnostic tests, an increasing number of new cases is observed. That is why it is very important to search new markers that would show high diagnostic utility. Therefore, we made an attempt to assess the usefulness of eotaxins, as there are few studies that investigate their significance, in patients with CRC. The study included 80 subjects (CRC patients and healthy volunteers). Serum concentrations of all eotaxins were measured using a multiplexing method (Luminex), while CCR3 was measured by immunoenzymatic assay (ELISA). CRP levels were determined by immunoturbidimetry and classical tumor marker levels (CEA and CA 19-9) and were measured using chemiluminescent microparticle immunoassay (CMIA). The highest usefulness among the proteins tested showed CCR3. Its concentrations were significantly higher in the CRC group than in healthy controls. The diagnostic sensitivity, specificity, positive and negative predictive value, and the area under the ROC curve (AUC) of CCR3 were higher than those of CA 19-9. The maximum values for sensitivity, negative predictive value, and AUC were obtained for a combination of CCR3 and CRP. Our findings suggest the potential usefulness of CCR3 in the diagnosis of CRC, especially in combination with CRP or CEA.

scholarly journals Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 973
Author(s):  
Valentina Giannini ◽  
Simone Mazzetti ◽  
Giovanni Cappello ◽  
Valeria Maria Doronzio ◽  
Lorenzo Vassallo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Computer Aided Diagnosis ◽  
Multiparametric Mri ◽  
Performance Study ◽  
Predictive Values ◽  
Cad System ◽  
Computer Aided ◽  
Clinically Significant ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time.

Sinai and Exodus: Two Grounds for Hope in the Jewish Tradition

1978 ◽  
Vol 14 (3) ◽  
pp. 373-387
Author(s):  
David Hartman
Keyword(s):  
Jewish Tradition ◽  
Look Ahead ◽  
The World ◽  
The Future ◽  
Sole Criterion ◽  
The Given ◽  
Better Than

Hope is a category of transcedence, by means of which a man does not permit what he senses and experiences to be the sole criterion of what is possible. It is the belief or the conviction that present reality (what I see) does not exhaust the potentialities of the given data. Hope opens the present to the future; it enables a man to look ahead, to break the fixity of what he observes, and to perceive the world as open-textured. The categories of possibility and of transcendence interweave a closely stitched fabric - hope says that tomorrow can be better than today.

Molecular markers and the future of colorectal cancer screening

2013 ◽  
Vol 2 (2) ◽  
pp. 95-97
Author(s):  
Beatriz Carvalho ◽  
Linda JW Bosch ◽  
Manon Van Engeland ◽  
Gerrit A Meijer
Keyword(s):  
Colorectal Cancer ◽  
Molecular Markers ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
The Future

Convolutional Neural Networks in Computer-Aided Diagnosis of Colorectal Polyps and Cancer: A Review

2021 ◽  
Author(s):  
Kamyab Keshtkar
Keyword(s):  
Colorectal Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Support Vector ◽  
Training Process ◽  
Computer Aided ◽  
Conventional Models ◽  
Aided Diagnosis

As a relatively high percentage of adenoma polyps are missed, a computer-aided diagnosis (CAD) tool based on deep learning can aid the endoscopist in diagnosing colorectal polyps or colorectal cancer in order to decrease polyps missing rate and prevent colorectal cancer mortality. Convolutional Neural Network (CNN) is a deep learning method and has achieved better results in detecting and segmenting specific objects in images in the last decade than conventional models such as regression, support vector machines or artificial neural networks. In recent years, based on the studies in medical imaging criteria, CNN models have acquired promising results in detecting masses and lesions in various body organs, including colorectal polyps. In this review, the structure and architecture of CNN models and how colonoscopy images are processed as input and converted to the output are explained in detail. In most primary studies conducted in the colorectal polyp detection and classification field, the CNN model has been regarded as a black box since the calculations performed at different layers in the model training process have not been clarified precisely. Furthermore, I discuss the differences between the CNN and conventional models, inspect how to train the CNN model for diagnosing colorectal polyps or cancer, and evaluate model performance after the training process.

Recent Reviews on Machine Vision-Based 3D Reconstruction

2021 ◽  
Vol 14 ◽  
Author(s):  
Hongxin Zhang ◽  
Shaowei Ma ◽  
Meng Li ◽  
Hanghang Jiang ◽  
Jiaming Li
Keyword(s):  
Machine Vision ◽  
3D Reconstruction ◽  
Autonomous Navigation ◽  
Robot Vision ◽  
Research Direction ◽  
Development Trend ◽  
Future Research ◽  
The Future ◽  
The Development Trend ◽  
Better Than

Background: In machine vision, the 3D reconstruction is widely used in medical system, autonomous navigation, aviation and remote sensing measurement, industrial automation and other fields, and the demand for reconstruction precision is significantly highlighted. Therefore, the 3D reconstruction is of great research value and will be an important research direction in the future. Objective: By reviewing the latest development and patent of 3D reconstruction, this paper provides references to researchers in related fields. Methods: Machine vision-based 3D reconstruction patents and literatures were analyzed from the aspects of the algorithm, innovation and application. Among them, there are more than 30 patents and nearly 30 literatures in the past ten years. Results: Researches on machine vision-based 3D reconstruction in recent 10 years are reviewed, and the typical characteristics were concluded. The main problems in its development were analyzed, the development trend was foreseen, and the current and future research on the productions and patents related to machine vision-based 3D reconstruction is discussed. Conclusion: The reconstruction result of binocular vision and multi-vision is better than monocular vision in most cases. Current researches of 3D reconstruction focus on robot vision navigation, intelligent vehicle environment sensing system and virtual reality. The aspects that need to be improved in the future include: improving robustness, reducing computational complexity, and reducing operating equipment requirements, and so on. Furthermore, more patents on machine vision-based 3D reconstruction also should be invented.

An improved YOLOv3 model for detecting location information of ovarian cancer from CT images

2021 ◽  
Vol 25 (6) ◽  
pp. 1565-1578
Author(s):  
Xun Wang ◽  
Hanlin Li ◽  
Lisheng Wang ◽  
Yongzhi Yu ◽  
Hao Zhou ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Cells ◽  
Ovarian Cancer Cells ◽  
Location Information ◽  
Learning Technology ◽  
Medical College ◽  
Geometric Deformation ◽  
Women's Lives ◽  
Aided Diagnosis ◽  
Better Than

Ovarian cancer is a malignant tumor that poses a serious threat to women’s lives. Computer-aided diagnosis (CAD) systems can classify the type of ovarian tumors, but few of them can provide exactly the location information of ovarian cancer cells. Recently, deep learning technology becomes hot for automatic detection of cancer cells, particularly for detecting their locations. In this work, we propose a novel end-to-end network YOLO-OC (Ovarian cancer) model, which can extract the characteristics of ovarian cancer more efficiently. In our method, deformable convolution is used to enhance the model’s ability to learn geometric deformation in space. Squeeze-and-Excitation (SE) module is proposed to automatically learn the importance of different channel features. Data experiments are conducted on datasets collected from The Affiliated Hospital of Qingdao University Medical College, China. Experimental results show that our YOLO-OC model achieves 91.83%, 85.66% and 73.82% on mean average precision [email protected], [email protected] and mAP@[.5,.95], respectively, which performs better than Faster R-CNN, SSD and RetinaNet on both accuracy and efficiency.

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