Abdominal Computed Tomography Localizer Image Generation: A Deep Learning Approach

2021 ◽  
pp. 106575
Author(s):  
Zongxi Liu ◽  
Huimin Zhao ◽  
Xiang Fang ◽  
Donglai Huo
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Learning Approach ◽  
Image Generation ◽  
Abdominal Computed Tomography

scholarly journals Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kwang-Hyun Uhm ◽  
Seung-Won Jung ◽  
Moon Hyung Choi ◽  
Hong-Kyu Shin ◽  
Jae-Ik Yoo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Kidney Cancer ◽  
Renal Tumor ◽  
Renal Tumors ◽  
Abdominal Computed Tomography ◽  
Wide Range ◽  
End To End ◽  
Multi Phase ◽  
Histologic Subtypes

AbstractIn 2020, it is estimated that 73,750 kidney cancer cases were diagnosed, and 14,830 people died from cancer in the United States. Preoperative multi-phase abdominal computed tomography (CT) is often used for detecting lesions and classifying histologic subtypes of renal tumor to avoid unnecessary biopsy or surgery. However, there exists inter-observer variability due to subtle differences in the imaging features of tumor subtypes, which makes decisions on treatment challenging. While deep learning has been recently applied to the automated diagnosis of renal tumor, classification of a wide range of subtype classes has not been sufficiently studied yet. In this paper, we propose an end-to-end deep learning model for the differential diagnosis of five major histologic subtypes of renal tumors including both benign and malignant tumors on multi-phase CT. Our model is a unified framework to simultaneously identify lesions and classify subtypes for the diagnosis without manual intervention. We trained and tested the model using CT data from 308 patients who underwent nephrectomy for renal tumors. The model achieved an area under the curve (AUC) of 0.889, and outperformed radiologists for most subtypes. We further validated the model on an independent dataset of 184 patients from The Cancer Imaging Archive (TCIA). The AUC for this dataset was 0.855, and the model performed comparably to the radiologists. These results indicate that our model can achieve similar or better diagnostic performance than radiologists in differentiating a wide range of renal tumors on multi-phase CT.

Exploring Techniques for Photo-realistic Image Generation from 3D Models - A Deep Learning Approach

2021 ◽  
Author(s):  
Pranoy Ghosh ◽  
Krithika M Pai ◽  
Manohara Pai M M ◽  
Ujjwal Verma ◽  
Frederic Rivet ◽  
...  
Keyword(s):  
Deep Learning ◽  
3D Models ◽  
Learning Approach ◽  
Image Generation ◽  
Realistic Image
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