scholarly journals Computer-Aided Detection (CADe) System with Optical Coherent Tomography for Melanin Morphology Quantification in Melasma Patients

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1498
Author(s):  
I-Ling Chen ◽  
Yen-Jen Wang ◽  
Chang-Cheng Chang ◽  
Yu-Hung Wu ◽  
Chih-Wei Lu ◽  
...  
Keyword(s):  
Computer Aided Detection ◽  
Full Field ◽  
Asian Patients ◽  
Pigmentary Disorders ◽  
Area Distribution ◽  
Computer Aided ◽  
Time Observation ◽  
Cade System ◽  
Distribution Intensity ◽  
Perilesional Skin

Dark skin-type individuals have a greater tendency to have pigmentary disorders, among which melasma is especially refractory to treat and often recurs. Objective measurement of melanin amount helps evaluate the treatment response of pigmentary disorders. However, naked-eye evaluation is subjective to weariness and bias. We used a cellular resolution full-field optical coherence tomography (FF-OCT) to assess melanin features of melasma lesions and perilesional skin on the cheeks of eight Asian patients. A computer-aided detection (CADe) system is proposed to mark and quantify melanin. This system combines spatial compounding-based denoising convolutional neural networks (SC-DnCNN), and through image processing techniques, various types of melanin features, including area, distribution, intensity, and shape, can be extracted. Through evaluations of the image differences between the lesion and perilesional skin, a distribution-based feature of confetti melanin without layering, two distribution-based features of confetti melanin in stratum spinosum, and a distribution-based feature of grain melanin at the dermal–epidermal junction, statistically significant findings were achieved (p-values = 0.0402, 0.0032, 0.0312, and 0.0426, respectively). FF-OCT enables the real-time observation of melanin features, and the CADe system with SC-DnCNN was a precise and objective tool with which to interpret the area, distribution, intensity, and shape of melanin on FF-OCT images.

scholarly journals Improved computer-aided detection of pulmonary nodules via deep learning in the sinogram domain

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Yongfeng Gao ◽  
Jiaxing Tan ◽  
Zhengrong Liang ◽  
Lihong Li ◽  
Yumei Huo
Keyword(s):  
Deep Learning ◽  
Area Under The Curve ◽  
Pulmonary Nodules ◽  
Ct Image ◽  
Computer Aided Detection ◽  
Ct Scanner ◽  
Human Visual Perception ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Cade System

AbstractComputer aided detection (CADe) of pulmonary nodules plays an important role in assisting radiologists’ diagnosis and alleviating interpretation burden for lung cancer. Current CADe systems, aiming at simulating radiologists’ examination procedure, are built upon computer tomography (CT) images with feature extraction for detection and diagnosis. Human visual perception in CT image is reconstructed from sinogram, which is the original raw data acquired from CT scanner. In this work, different from the conventional image based CADe system, we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain. Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain, we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram. The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database, with each case having at least one juxtapleural nodule annotation. Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve (AUC) of receiver operating characteristic based on sinogram alone, comparing to 0.89 based on CT image alone. Moreover, a combination of sinogram and CT image could further improve the value of AUC to 0.92. This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.

Computer-aided detection in direct digital full-field mammography: initial results

2002 ◽  
Vol 12 (12) ◽  
pp. 3015-3017 ◽  
Author(s):  
F. Baum ◽  
U. Fischer ◽  
S. Obenauer ◽  
E. Grabbe
Keyword(s):  
Computer Aided Detection ◽  
Full Field ◽  
Computer Aided ◽  
Initial Results

scholarly journals Detection of Breast Cancer with a Computer-Aided Detection Applied to Full-Field Digital Mammography

2013 ◽  
Vol 26 (4) ◽  
pp. 768-773 ◽  
Author(s):  
Ryusuke Murakami ◽  
Shinichiro Kumita ◽  
Hitomi Tani ◽  
Tamiko Yoshida ◽  
Kenichi Sugizaki ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Digital Mammography ◽  
Computer Aided Detection ◽  
Full Field ◽  
Full Field Digital Mammography ◽  
Computer Aided

Computer-aided Detection in Full-Field Digital Mammography: Sensitivity and Reproducibility in Serial Examinations

Radiology ◽  
2008 ◽  
Vol 246 (1) ◽  
pp. 71-80 ◽  
Author(s):  
Seung Ja Kim ◽  
Woo Kyung Moon ◽  
Nariya Cho ◽  
Joo Hee Cha ◽  
Sun Mi Kim ◽  
...  
Keyword(s):  
Digital Mammography ◽  
Computer Aided Detection ◽  
Full Field ◽  
Full Field Digital Mammography ◽  
Computer Aided

scholarly journals Computer-Aided Detection False Positives in Colonoscopy

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1113
Author(s):  
Yu-Hsi Hsieh ◽  
Chia-Pei Tang ◽  
Chih-Wei Tseng ◽  
Tu-Liang Lin ◽  
Felix W. Leung
Keyword(s):  
False Positives ◽  
Computer Aided Detection ◽  
Delphi Consensus ◽  
Detection Rates ◽  
Modified Delphi ◽  
Use Of Resources ◽  
Adenoma Detection ◽  
Computer Aided ◽  
Cade System ◽  
Meta Analyses

Randomized control trials and meta-analyses comparing colonoscopies with and without computer-aided detection (CADe) assistance showed significant increases in adenoma detection rates (ADRs) with CADe. A major limitation of CADe is its false positives (FPs), ranked 3rd in importance among 59 research questions in a modified Delphi consensus review. The definition of FPs varies. One commonly used definition defines an FP as an activation of the CADe system, irrespective of the number of frames or duration of time, not due to any polypoid or nonpolypoid lesions. Although only 0.07 to 0.2 FPs were observed per colonoscopy, video analysis studies using FPs as the primary outcome showed much higher numbers of 26 to 27 per colonoscopy. Most FPs were of short duration (91% < 0.5 s). A higher number of FPs was also associated with suboptimal bowel preparation. The appearance of FPs can lead to user fatigue. The polypectomy of FPs results in increased procedure time and added use of resources. Re-training the CADe algorithms is one way to reduce FPs but is not practical in the clinical setting during colonoscopy. Water exchange (WE) is an emerging method that the colonoscopist can use to provide salvage cleaning during insertion. We discuss the potential of WE for reducing FPs as well as the augmentation of ADRs through CADe.

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