scholarly journals A Sparse Deep Learning Approach for Automatic Segmentation of Human Vasculature in Multispectral Optoacoustic Tomography

2019 ◽  
Author(s):  
Nikolaos-Kosmas Chlis ◽  
Angelos Karlas ◽  
Nikolina-Alexia Fasoula ◽  
Michael Kallmayer ◽  
Hans-Henning Eckstein ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
Isosbestic Point ◽  
Learning Approach ◽  
Manual Segmentation ◽  
Signal Attenuation ◽  
Optoacoustic Tomography ◽  
Vascular Assessment ◽  
Tissue Interactions ◽  
Vascular Segmentation

AbstractMultispectral Optoacoustic Tomography (MSOT) resolves oxy- (HbO2) and deoxy-hemoglobin (Hb) to perform vascular imaging. MSOT suffers from gradual signal attenuation with depth due to light-tissue interactions: an effect that hinders the precise manual segmentation of vessels. Furthermore, vascular assessment requires functional tests, which last several minutes and result in recording thousands of images. Here, we introduce a deep learning approach with a sparse UNET (S-UNET) for automatic vascular segmentation in MSOT images to avoid the rigorous and time-consuming manual segmentation. We evaluated the S-UNET on a test-set of 33 images, achieving a median DICE score of 0.88. Apart from high segmentation performance, our method based its decision on two wavelengths with physical meaning for the task-at-hand: 850 nm (peak absorption of oxy-hemoglobin) and 810 nm (isosbestic point of oxy-and deoxy-hemoglobin). Thus, our approach achieves precise data-driven vascular segmentation for automated vascular assessment and may boost MSOT further towards its clinical translation.

scholarly journals A sparse deep learning approach for automatic segmentation of human vasculature in multispectral optoacoustic tomography

2020 ◽  
Vol 20 ◽  
pp. 100203 ◽  
Author(s):  
Nikolaos-Kosmas Chlis ◽  
Angelos Karlas ◽  
Nikolina-Alexia Fasoula ◽  
Michael Kallmayer ◽  
Hans-Henning Eckstein ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
Learning Approach ◽  
Optoacoustic Tomography

scholarly journals Deep learning approach for automatic segmentation of ulna and radius in dual-energy X-ray imaging

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Fan Yang ◽  
Xin Weng ◽  
Yuehong Miao ◽  
Yuhui Wu ◽  
Hong Xie ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
Dual Energy ◽  
Learning Approach ◽  
Mineral Density ◽  
X Ray ◽  
X Ray Imaging ◽  
Residual Block ◽  
Deep Learning Model ◽  
Fold Cross Validation

Abstract Background Segmentation of the ulna and radius is a crucial step for the measurement of bone mineral density (BMD) in dual-energy X-ray imaging in patients suspected of having osteoporosis. Purpose This work aimed to propose a deep learning approach for the accurate automatic segmentation of the ulna and radius in dual-energy X-ray imaging. Methods and materials We developed a deep learning model with residual block (Resblock) for the segmentation of the ulna and radius. Three hundred and sixty subjects were included in the study, and five-fold cross-validation was used to evaluate the performance of the proposed network. The Dice coefficient and Jaccard index were calculated to evaluate the results of segmentation in this study. Results The proposed network model had a better segmentation performance than the previous deep learning-based methods with respect to the automatic segmentation of the ulna and radius. The evaluation results suggested that the average Dice coefficients of the ulna and radius were 0.9835 and 0.9874, with average Jaccard indexes of 0.9680 and 0.9751, respectively. Conclusion The deep learning-based method developed in this study improved the segmentation performance of the ulna and radius in dual-energy X-ray imaging.

scholarly journals Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy

2021 ◽  
Vol 11 ◽  
Author(s):  
Xi Liu ◽  
Kai-Wen Li ◽  
Ruijie Yang ◽  
Li-Sheng Geng
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Gross Tumor Volume ◽  
Tumor Volume ◽  
Automatic Segmentation ◽  
Treatment Modalities ◽  
Dice Similarity Coefficient ◽  
Manual Segmentation ◽  
Segmentation Technique ◽  
Radiation Oncologists

Lung cancer is the leading cause of cancer-related mortality for males and females. Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While delivering the prescribed dose to tumor targets, it is essential to spare the tissues near the targets—the so-called organs-at-risk (OARs). An optimal RT planning benefits from the accurate segmentation of the gross tumor volume and surrounding OARs. Manual segmentation is a time-consuming and tedious task for radiation oncologists. Therefore, it is crucial to develop automatic image segmentation to relieve radiation oncologists of the tedious contouring work. Currently, the atlas-based automatic segmentation technique is commonly used in clinical routines. However, this technique depends heavily on the similarity between the atlas and the image segmented. With significant advances made in computer vision, deep learning as a part of artificial intelligence attracts increasing attention in medical image automatic segmentation. In this article, we reviewed deep learning based automatic segmentation techniques related to lung cancer and compared them with the atlas-based automatic segmentation technique. At present, the auto-segmentation of OARs with relatively large volume such as lung and heart etc. outperforms the organs with small volume such as esophagus. The average Dice similarity coefficient (DSC) of lung, heart and liver are over 0.9, and the best DSC of spinal cord reaches 0.9. However, the DSC of esophagus ranges between 0.71 and 0.87 with a ragged performance. In terms of the gross tumor volume, the average DSC is below 0.8. Although deep learning based automatic segmentation techniques indicate significant superiority in many aspects compared to manual segmentation, various issues still need to be solved. We discussed the potential issues in deep learning based automatic segmentation including low contrast, dataset size, consensus guidelines, and network design. Clinical limitations and future research directions of deep learning based automatic segmentation were discussed as well.

scholarly journals Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 245
Author(s):  
Seok Oh ◽  
Young-Jae Kim ◽  
Young-Taek Park ◽  
Kwang-Gi Kim
Keyword(s):  
Deep Learning ◽  
Pancreatic Cyst ◽  
Automatic Segmentation ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Learning Approach ◽  
Lesion Segmentation ◽  
Significant Difference ◽  
The Cross ◽  
External Test

The automatic segmentation of the pancreatic cyst lesion (PCL) is essential for the automated diagnosis of pancreatic cyst lesions on endoscopic ultrasonography (EUS) images. In this study, we proposed a deep-learning approach for PCL segmentation on EUS images. We employed the Attention U-Net model for automatic PCL segmentation. The Attention U-Net was compared with the Basic U-Net, Residual U-Net, and U-Net++ models. The Attention U-Net showed a better dice similarity coefficient (DSC) and intersection over union (IoU) scores than the other models on the internal test. Although the Basic U-Net showed a higher DSC and IoU scores on the external test than the Attention U-Net, there was no statistically significant difference. On the internal test of the cross-over study, the Attention U-Net showed the highest DSC and IoU scores. However, there was no significant difference between the Attention U-Net and Residual U-Net or between the Attention U-Net and U-Net++. On the external test of the cross-over study, all models showed no significant difference from each other. To the best of our knowledge, this is the first study implementing segmentation of PCL on EUS images using a deep-learning approach. Our experimental results show that a deep-learning approach can be applied successfully for PCL segmentation on EUS images.

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