scholarly journals Unsupervised Approaches for the Segmentation of Dry ARMD Lesions in Eye Fundus cSLO Images

2021 ◽  
Vol 7 (8) ◽  
pp. 143
Author(s):  
Clément Royer ◽  
Jérémie Sublime ◽  
Florence Rossant ◽  
Michel Paques
Keyword(s):  
Automatic Segmentation ◽  
Age Related Macular Degeneration ◽  
Stable Model ◽  
Learning Method ◽  
Convolutional Network ◽  
Age Related ◽  
Supervised Segmentation ◽  
Segmentation Methods ◽  
Severity Of The Disease ◽  
Classical Computer

Age-related macular degeneration (ARMD), a major cause of sight impairment for elderly people, is still not well understood despite intensive research. Measuring the size of the lesions in the fundus is the main biomarker of the severity of the disease and as such is widely used in clinical trials yet only relies on manual segmentation. Artificial intelligence, in particular automatic image analysis based on neural networks, has a major role to play in better understanding the disease, by analyzing the intrinsic optical properties of dry ARMD lesions from patient images. In this paper, we propose a comparison of automatic segmentation methods (classical computer vision method, machine learning method and deep learning method) in an unsupervised context applied on cSLO IR images. Among the methods compared, we propose an adaptation of a fully convolutional network, called W-net, as an efficient method for the segmentation of ARMD lesions. Unlike supervised segmentation methods, our algorithm does not require annotated data which are very difficult to obtain in this application. Our method was tested on a dataset of 328 images and has shown to reach higher quality results than other compared unsupervised methods with a F1 score of 0.87, while having a more stable model, even though in some specific cases, texture/edges-based methods can produce relevant results.

scholarly journals Automatic segmentation of the borders of the pathological focus in the macular area of the retina to calculate the area of the pathological focus

2021 ◽  
Vol 2131 (2) ◽  
pp. 022081
Author(s):  
V A Filippenko ◽  
U F Bondarenko ◽  
V V Dolgov ◽  
A N Epikhin
Keyword(s):  
Automatic Segmentation ◽  
Correct Diagnosis ◽  
Age Related Macular Degeneration ◽  
Contact Method ◽  
Macular Area ◽  
Cross Sectional ◽  
Human Eye ◽  
Age Related ◽  
Pathological Focus ◽  
Almost All

Abstract Optical coherence tomography (OCT) is a modern, non-contact method of diagnostic examination that enables the visualization of various tissues of the human eye in a cross-sectional view at the microscopic level with the required morphological information. The software pre-installed in such devices contains many different tools for analyzing scans and has almost all the functionality necessary for the doctor to make a correct diagnosis. However, as time passes, more and more advanced methods of analyzing the images appear, which actualizes new tasks of developing additional software that can supplement and expand the functionality of the diagnostic equipment. This paper proposes an algorithm for automatic segmentation of the borders of a pathological focus in the macular area of the retina to calculate the area of the pathological focus, which together with other algorithms for analysis of morphometric parameters of the human eye, which are still under development, will be used for more accurate diagnosis of the stage of age-related macular degeneration.

scholarly journals Automatic Segmentation of Prostate Cancer using cascaded Fully Convolutional Network

2021 ◽  
Vol 309 ◽  
pp. 01068
Author(s):  
Padmavathi Kora ◽  
K Reddy Madhavi ◽  
J Avanija ◽  
Sunitha Gurram ◽  
K Meenakshi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Neural Networks ◽  
Automatic Segmentation ◽  
Learning Method ◽  
Extraction Techniques ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Tumour Detection ◽  
Prostate Segmentation ◽  
Learning Techniques

In this paper we proposed a prostate segmentation and also tumour detection using deep neural networks. The cutting-edge deep learning techniques are useful compared to the challenges of machine learning based feature extraction techniques. Here we proposed a strategy that contains an FCN model that incorporates data from several MRI images, allowing for faster convergence and more accurate segmentation. T1 and DWI volumes may be used together to delineate the prostate boundary, according to this study. Second, we investigated whether this method might be utilized to provide voxel-level prostate tumor forecasts. The cascaded learning method and performed tests to demonstrate its effectiveness.

scholarly journals Automated Retinal Boundary Segmentation of Optical Coherence Tomography Images Using an Improved Canny Operator

2021 ◽  
Author(s):  
Jian Liu ◽  
Shixin Yan ◽  
Nan Lu ◽  
Dongni Yang ◽  
Hongyu Lv ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Automatic Segmentation ◽  
Initial Boundary ◽  
Boundary Detection ◽  
Structural Features ◽  
Detection Algorithm ◽  
Age Related Macular Degeneration ◽  
Canny Operator ◽  
Age Related ◽  
Retinal Segmentation

Abstract Retinal segmentation is a prerequisite for quantifying retinal structural features and diagnosing related ophthalmic diseases. Canny operator is recognized as the best boundary detection operator so far, and is often used to obtain the initial boundary of the retina in retinal segmentation. However, the traditional Canny operator is susceptible to vascular shadows, vitreous artifacts, or noise interference in retinal segmentation, causing serious misdetection or missed detection. This paper proposed an improved Canny operator for automatic segmentation of retinal boundaries. The improved algorithm solves the problems of the traditional Canny operator by adding a multi-point boundary search step on the basis of the original method, and adjusts the convolution kernel. The algorithm was used to segment the retinal images of healthy subjects and age-related macular degeneration (AMD) patients; eleven retinal boundaries were identified and compared with the results of manual segmentation by the ophthalmologists. The average difference between the automatic and manual methods is: 2-6 microns (1~2 pixels) for healthy subjects and 3-10 microns (1~3 pixels) for AMD patients. Qualitative method is also used to verify the accuracy and stability of the algorithm. The percentage of “perfect segmentation” and “good segmentation” is 98% in healthy subjects and 94% in AMD patients. This algorithm can be used alone or in combination with other methods as an initial boundary detection algorithm. It is easy to understand and improve, and may become a useful tool for analyzing and diagnosing eye diseases.

scholarly journals Comparative Study: Statistical Approach and Deep Learning Method for Automatic Segmentation Methods for Lung CT Image Segmentation

2020 ◽  
Vol 2 (4) ◽  
pp. 187-193
Author(s):  
Dr. Akey Sungheetha ◽  
Dr. Rajesh Sharma R
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Comparative Study ◽  
Performance Metrics ◽  
Automatic Segmentation ◽  
Ct Image ◽  
Learning Method ◽  
Segmentation Methods ◽  
Sensitivity Specificity ◽  
Lung Ct

Recently, deep learning technique is playing important starring role for image segmentation field in medical imaging of accurate tasks. In a critical component of diagnosis, deep learning is an organized network with homogeneous areas to provide accurate results. It is proved its superior quality with statistical model automatic segmentation methods in many critical condition environments. In this research article, we focus the improved accuracy and speed of the system process compared with conservative automatic segmentation methods. Also we compared performance metrics such as accuracy, sensitivity, specificity, precision, RMSE, Precision- Recall Curve with different algorithm in deep learning method. This comparative study covers the constructing an efficient and accurate model for Lung CT image segmentation.

Optical coherence tomography angiography reproducibility of lesion size measurements in neovascular age-related macular degeneration (AMD)

2017 ◽  
Vol 102 (6) ◽  
pp. 821-826 ◽  
Author(s):  
Francesca Amoroso ◽  
Alexandra Miere ◽  
Oudy Semoun ◽  
Camille Jung ◽  
Vittorio Capuano ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Macular Degeneration ◽  
Optical Coherence Tomography Angiography ◽  
Automatic Segmentation ◽  
Choroidal Neovascularisation ◽  
Age Related Macular Degeneration ◽  
Optical Coherence ◽  
Lesion Area ◽  
Vessel Area ◽  
Age Related

PurposeTo evaluate the reproducibility and interuser agreement of measurements of choroidal neovascularisation in optical coherence tomography angiography (OCTA).DesignProspective non-interventional study.MethodsConsecutive patients, presenting with neovascular age-related macular degeneration (AMD), underwent two sequential OCTA examinations (AngioVue, Optovue, Fremont, California, USA), performed by the same trained examiner. Neovascular lesion area was then measured on both examinations in the choriocapillaris automatic segmentation by two masked readers, using the semiautomated measuring software embedded in the instrument. Two measuring features were used: the first corresponding to the total manually contoured lesion area with the flow draw tool (select area) and the second to the total area of solely vessels with high flow within the lesion (vessel area). These measurements were then compared in order to assess both the reproducibility of OCTA examination and the interuser agreement with the embedded software.ResultsForty-eight eyes of 46 patients (77.4 mean age,+/-8.2 SD, range from 62 to 95 years old, eight men, 38 women) were included in our study. Mean choroidal neovascularisation area was of 0.72+/-0.7 mm2 for the first measurement and 0.75+/-0.76 mm2 for the second measurement; difference between the first and the second measurement was 0.03 mm2. Intrauser agreement was of 0.98 (CI 0.98 to 0.99) for both ‘vessel area’ and ‘select area’ features. Interuser agreement was of 0.98 (CI 0.97 to 0.99) for ‘select area’ and ‘vessel area’ features.ConclusionOur data suggest that OCTA provide reproducible imaging for evaluation of the neovascular size in the setting of AMD.

Age related macular degeneration (ARMD) – pathophysiological and clinical features and therapeutic concepts

2001 ◽  
Vol 58 (1) ◽  
pp. 28-35 ◽  
Author(s):  
Ursula Körner-Stiefbold
Keyword(s):  
Macular Degeneration ◽  
Clinical Features ◽  
Altersbedingte Makuladegeneration ◽  
Age Related Macular Degeneration ◽  
Genetische Faktoren ◽  
Age Related ◽  
Therapeutic Concepts

Die altersbedingte Makuladegeneration (AMD) ist eine der häufigsten Ursachen für einen irreversiblen Visusverlust bei Patienten über 65 Jahre. Nahezu 30% der über 75-Jährigen sind von einer AMD betroffen. Trotz neuer Erkenntnisse in der Grundlagenforschung ist die Ätiologie, zu der auch genetische Faktoren gehören, noch nicht völlig geklärt. Aus diesem Grund sind die Behandlungsmöglichkeiten zum jetzigen Zeitpunkt noch limitiert, so dass man lediglich von Therapieansätzen sprechen kann. Die derzeit zur Verfügung stehenden Möglichkeiten wie medikamentöse, chirurgische und laser- und strahlentherapeutische Maßnahmen werden beschrieben.

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