scholarly journals Improving image segmentation performance and quantitative analysis via a computer-aided grading methodology for optical coherence tomography retinal image analysis

2010 ◽  
Vol 15 (4) ◽  
pp. 046015 ◽  
Author(s):  
Delia Cabrera Debuc ◽  
Harry M. Salinas ◽  
Sudarshan Ranganathan ◽  
Erika Tátrai ◽  
Wei Gao ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Image Analysis ◽  
Image Segmentation ◽  
Quantitative Analysis ◽  
Retinal Image ◽  
Optical Coherence ◽  
Retinal Image Analysis ◽  
Computer Aided

scholarly journals Reliability and reproducibility of macular segmentation using a custom-built optical coherence tomography retinal image analysis software

2009 ◽  
Vol 14 (6) ◽  
pp. 064023 ◽  
Author(s):  
Delia Cabrera DeBuc ◽  
Gábor Márk Somfai ◽  
Sudarshan Ranganathan ◽  
Erika Tátrai ◽  
Mária Ferencz ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Image Analysis ◽  
Retinal Image ◽  
Optical Coherence ◽  
Analysis Software ◽  
Retinal Image Analysis ◽  
Image Analysis Software

Automatic Retinal Image Analysis for the Detection of Diabetic Retinopathy

2018 ◽  
pp. 146-161 ◽  
Author(s):  
Prasanna Porwal ◽  
Samiksha Pachade ◽  
Manesh Kokare ◽  
Girish Deshmukh ◽  
Vivek Sahasrabuddhe
Keyword(s):  
Image Analysis ◽  
Diabetic Retinopathy ◽  
Retinal Image ◽  
Communication Systems ◽  
Retinal Disease ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Retinal Image Analysis ◽  
Computer Aided ◽  
Computer Scientists

Diabetic Retinopathy, a condition in the person affected by diabetes, is most common cause of blindness in the world. Recent research has given a better understanding of requirement in clinical eye care practice to identify better and cheaper ways of identification, management, diagnosis and treatment of retinal disease. The importance of diabetic retinopathy screening programs and difficulty in achieving reliable early diagnosis of diabetic retinopathy at a reasonable cost needs attention to develop computer-aided diagnosis tool. Computer aided disease diagnosis in retinal image analysis could ease mass screening of population with diabetes mellitus and help clinicians in utilizing their time more efficiently. The recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities to the biomedical engineers and computer scientists to meet the requirements of clinical practice. With proper self-care, management, and medical professional support, individuals with diabetes can live a healthy and long life.

scholarly journals Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Peter M. Maloca ◽  
Philipp L. Müller ◽  
Aaron Y. Lee ◽  
Adnan Tufail ◽  
Konstantinos Balaskas ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Tomography Image

AbstractMachine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization (‘neural recording’). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications.

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