scholarly journals Automatic segmentation of rectal tumor on diffusion‐weighted images by deep learning with U‐Net

2021 ◽  
Author(s):  
Hai‐Tao Zhu ◽  
Xiao‐Yan Zhang ◽  
Yan‐Jie Shi ◽  
Xiao‐Ting Li ◽  
Ying‐Shi Sun
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
Rectal Tumor ◽  
Diffusion Weighted Images ◽  
Diffusion Weighted

scholarly journals Fully automatic segmentation of the dentate nucleus using diffusion weighted images

2012 ◽  
Author(s):  
Chuyang Ye ◽  
John A. Bogovic ◽  
Pierre-Louis Bazin ◽  
Jerry L. Prince ◽  
Sarah H. Ying
Keyword(s):  
Dentate Nucleus ◽  
Automatic Segmentation ◽  
Diffusion Weighted Images ◽  
Diffusion Weighted ◽  
Fully Automatic

Early Ischemic Stroke: Comparison of Diffusion Weighted Images and ADC maps

1998 ◽  
Vol 39 (1) ◽  
pp. 21
Author(s):  
Soo Jung Choi ◽  
Choong Gon Choi ◽  
Jae Kyun Kim ◽  
Jung Hoon Kim ◽  
Jae Hong Lee ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Diffusion Weighted Images ◽  
Diffusion Weighted

scholarly journals A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jared Hamwood ◽  
Beat Schmutz ◽  
Michael J. Collins ◽  
Mark C. Allenby ◽  
David Alonso-Caneiro
Keyword(s):  
Deep Learning ◽  
Human Performance ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Human Observer ◽  
In Series ◽  
Magnetic Resonance Imaging Mri ◽  
High Degree ◽  
Orbital Region ◽  
Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

scholarly journals Integrated slice-specific dynamic shimming diffusion weighted imaging (DWI) for rectal Cancer detection and characterization

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jianxing Qiu ◽  
Jing Liu ◽  
Zhongxu Bi ◽  
Xiaowei Sun ◽  
Xin Wang ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Image Quality ◽  
Diffusion Weighted Imaging ◽  
Rectal Tumor ◽  
B Values ◽  
Primary Rectal Cancer ◽  
Diffusion Weighted ◽  
Adc Values ◽  
Group 2 ◽  
Group 1

Abstract Purpose To compare integrated slice-specific dynamic shimming (iShim) diffusion weighted imaging (DWI) and single-shot echo-planar imaging (SS-EPI) DWI in image quality and pathological characterization of rectal cancer. Materials and methods A total of 193 consecutive rectal tumor patients were enrolled for retrospective analysis. Among them, 101 patients underwent iShim-DWI (b = 0, 800, and 1600 s/mm2) and 92 patients underwent SS-EPI-DWI (b = 0, and 1000 s/mm2). Qualitative analyses of both DWI techniques was performed by two independent readers; including adequate fat suppression, the presence of artifacts and image quality. Quantitative analysis was performed by calculating standard deviation (SD) of the gluteus maximus, signal intensity (SI) of lesion and residual normal rectal wall, apparent diffusion coefficient (ADC) values (generated by b values of 0, 800 and 1600 s/mm2 for iShim-DWI, and by b values of 0 and 1000 s/mm2 for SS-EPI-DWI) and image quality parameters, such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of primary rectal tumor. For the primary rectal cancer, two pathological groups were divided according to pathological results: Group 1 (well-differentiated) and Group 2 (poorly differentiated). Statistical analyses were performed with p < 0.05 as significant difference. Results Compared with SS-EPI-DWI, significantly higher scores of image quality were obtained in iShim-DWI cases (P < 0.001). The SDbackground was significantly reduced on b = 1600 s/mm2 images and ADC maps of iShim-DWI. Both SNR and CNR of b = 800 s/mm2 and b = 1600 s/mm2 images in iShim-DWI were higher than those of b = 1000 s/mm2 images in SS-EPI-DWI. In primary rectal cancer of iShim-DWI cohort, SIlesion was significantly higher than SIrectum in both b = 800 and 1600 s/mm2 images. ADC values were significantly lower in Group 2 (0.732 ± 0.08) × 10− 3 mm2/s) than those in Group 1 ((0.912 ± 0.21) × 10− 3 mm2/s). ROC analyses showed significance of ADC values and SIlesion between the two groups. Conclusion iShim-DWI with b values of 0, 800 and 1600 s/mm2 is a promising technique of high image quality in rectal tumor imaging, and has potential ability to differentiate rectal cancer from normal wall and predicting pathological characterization.

Automatic segmentation of skeletal structures in X-ray images using deep learning for comparative radiography

2021 ◽  
pp. 200458
Author(s):  
Óscar Gómez ◽  
Pablo Mesejo ◽  
Óscar Ibáñez
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
X Ray ◽  
Skeletal Structures

scholarly journals Automatic Segmentation of Choroid Layer Using Deep Learning on Spectral Domain Optical Coherence Tomography

2021 ◽  
Vol 11 (12) ◽  
pp. 5488
Author(s):  
Wei Ping Hsia ◽  
Siu Lun Tse ◽  
Chia Jen Chang ◽  
Yu Len Huang
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Choroidal Thickness ◽  
Automatic Segmentation ◽  
Average Error ◽  
Good Prediction ◽  
Optical Coherence ◽  
Learning Method ◽  
Subfoveal Choroidal Thickness ◽  
Fully Connected

The purpose of this article is to evaluate the accuracy of the optical coherence tomography (OCT) measurement of choroidal thickness in healthy eyes using a deep-learning method with the Mask R-CNN model. Thirty EDI-OCT of thirty patients were enrolled. A mask region-based convolutional neural network (Mask R-CNN) model composed of deep residual network (ResNet) and feature pyramid networks (FPNs) with standard convolution and fully connected heads for mask and box prediction, respectively, was used to automatically depict the choroid layer. The average choroidal thickness and subfoveal choroidal thickness were measured. The results of this study showed that ResNet 50 layers deep (R50) model and ResNet 101 layers deep (R101). R101 U R50 (OR model) demonstrated the best accuracy with an average error of 4.85 pixels and 4.86 pixels, respectively. The R101 ∩ R50 (AND model) took the least time with an average execution time of 4.6 s. Mask-RCNN models showed a good prediction rate of choroidal layer with accuracy rates of 90% and 89.9% for average choroidal thickness and average subfoveal choroidal thickness, respectively. In conclusion, the deep-learning method using the Mask-RCNN model provides a faster and accurate measurement of choroidal thickness. Comparing with manual delineation, it provides better effectiveness, which is feasible for clinical application and larger scale of research on choroid.

A 3D deep learning approach based on Shape Prior for automatic segmentation of myocardial diseases

2020 ◽  
Author(s):  
Khawla Brahim ◽  
Abdul Qayyum ◽  
Alain Lalande ◽  
Arnaud Boucher ◽  
Anis Sakly ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
Learning Approach ◽  
Shape Prior ◽  
Myocardial Diseases
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