scholarly journals Automated segmentation of lung, liver, and liver tumors from Tc‐99m MAA SPECT/CT images for Y‐90 radioembolization using convolutional neural networks

2021 ◽  
Author(s):  
Anucha Chaichana ◽  
Eric C. Frey ◽  
Ajalaya Teyateeti ◽  
Kijja Rhoongsittichai ◽  
Chiraporn Tocharoenchai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Liver Tumors ◽  
Ct Images ◽  
Automated Segmentation

Automated segmentation of the knee for age assessment in 3D MR images using convolutional neural networks

2018 ◽  
Vol 133 (4) ◽  
pp. 1191-1205 ◽  
Author(s):  
Paul-Louis Pröve ◽  
Eilin Jopp-van Well ◽  
Ben Stanczus ◽  
Michael M. Morlock ◽  
Jochen Herrmann ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automated Segmentation ◽  
Mr Images ◽  
Age Assessment

scholarly journals Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks

2019 ◽  
Vol 12 (9) ◽  
pp. 848-852 ◽  
Author(s):  
Renan Sales Barros ◽  
Manon L Tolhuisen ◽  
Anna MM Boers ◽  
Ivo Jansen ◽  
Elena Ponomareva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Convolutional Neural Networks ◽  
Infarct Volume ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Reference Standard ◽  
Computed Tomography Images

Background and purposeInfarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in clinical practice.ObjectiveTo assess the value of convolutional neural networks (CNNs) in the automatic segmentation of infarct volume in follow-up CT images in a large population of patients with acute ischemic stroke.Materials and methodsWe included CT images of 1026 patients from a large pooling of patients with acute ischemic stroke. A reference standard for the infarct segmentation was generated by manual delineation. We introduce three CNN models for the segmentation of subtle, intermediate, and severe hypodense lesions. The fully automated infarct segmentation was defined as the combination of the results of these three CNNs. The results of the three-CNNs approach were compared with the results from a single CNN approach and with the reference standard segmentations.ResultsThe median infarct volume was 48 mL (IQR 15–125 mL). Comparison between the volumes of the three-CNNs approach and manually delineated infarct volumes showed excellent agreement, with an intraclass correlation coefficient (ICC) of 0.88. Even better agreement was found for severe and intermediate hypodense infarcts, with ICCs of 0.98 and 0.93, respectively. Although the number of patients used for training in the single CNN approach was much larger, the accuracy of the three-CNNs approach strongly outperformed the single CNN approach, which had an ICC of 0.34.ConclusionConvolutional neural networks are valuable and accurate in the quantitative assessment of infarct volumes, for both subtle and severe hypodense infarcts in follow-up CT images. Our proposed three-CNNs approach strongly outperforms a more straightforward single CNN approach.

Automatic Lung Nodule Detection in CT Images Using Convolutional Neural Networks

2019 ◽  
Vol E102.A (10) ◽  
pp. 1364-1373
Author(s):  
Furqan SHAUKAT ◽  
Kamran JAVED ◽  
Gulistan RAJA ◽  
Junaid MIR ◽  
Muhammad Laiq Ur Rahman SHAHID
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodule Detection ◽  
Nodule Detection
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