Deep-learning-based fast and fully automated segmentation on abdominal multiple organs from CT

2019 ◽  
Author(s):  
Jieun Kim ◽  
June-Goo Lee
Keyword(s):  
Deep Learning ◽  
Automated Segmentation ◽  
Multiple Organs

scholarly journals Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1952
Author(s):  
May Phu Paing ◽  
Supan Tungjitkusolmun ◽  
Toan Huy Bui ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Deep Learning ◽  
Brain Infarction ◽  
Three Dimensional ◽  
Dice Similarity Coefficient ◽  
Automated Segmentation ◽  
Variational Mode Decomposition ◽  
Brain Scans ◽  
Automated Method ◽  
Mode Decomposition ◽  
Segmentation Task

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

scholarly journals Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning

2020 ◽  
Vol 53 (1) ◽  
pp. 259-268 ◽  
Author(s):  
Lenhard Pennig ◽  
Ulrike Cornelia Isabel Hoyer ◽  
Lukas Goertz ◽  
Rahil Shahzad ◽  
Thorsten Persigehl ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Deep Learning ◽  
Clinical Evaluation ◽  
Central Nervous System Lymphoma ◽  
Multiparametric Mri ◽  
Automated Segmentation

Automated segmentation of the optic disc using the deep learning

2019 ◽  
Author(s):  
Lei Wang ◽  
Han Liu ◽  
Jian Zhang ◽  
Hang Chen ◽  
Jiantao Pu
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Automated Segmentation

scholarly journals Automated Segmentation of Abdominal Skeletal Muscle in Pediatric CT Scans Using Deep Learning

2021 ◽  
pp. e200130
Author(s):  
James Castiglione ◽  
Elanchezhian Somasundaram ◽  
Leah A. Gilligan ◽  
Andrew T. Trout ◽  
Samuel Brady
Keyword(s):  
Skeletal Muscle ◽  
Deep Learning ◽  
Ct Scans ◽  
Automated Segmentation ◽  
Pediatric Ct

scholarly journals Deep Learning Applied to Automated Segmentation of Geographic Atrophy in Fundus Autofluorescence Images

2021 ◽  
Vol 10 (8) ◽  
pp. 2
Author(s):  
Janan Arslan ◽  
Gihan Samarasinghe ◽  
Arcot Sowmya ◽  
Kurt K. Benke ◽  
Lauren A. B. Hodgson ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fundus Autofluorescence ◽  
Geographic Atrophy ◽  
Automated Segmentation

scholarly journals Segmentation for Complex Background Images using Deep Learning Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 1746-1750
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Vision System ◽  
Automated Segmentation ◽  
Computer Vision System ◽  
Important Stage ◽  
Deep Learning Algorithm ◽  
Complex Background ◽  
Learning Techniques

Segmentation is an important stage in any computer vision system. Segmentation involves discarding the objects which are not of our interest and extracting only the object of our interest. Automated segmentation has become very difficult when we have complex background and other challenges like illumination, occlusion etc. In this project we are designing an automated segmentation system using deep learning algorithm to segment images with complex background.

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