Advances in experimental medicine and biology
Jan 1, 2020
Advancements in musculoskeletal analysis have been achieved by adopting deep learning technology in image recognition and analysis. Unlike musculoskeletal modeling based on computational anatomy, deep learning-based methods can obtain muscle informat...
Advances in experimental medicine and biology
Jan 1, 2020
This chapter focuses on modern deep learning techniques that are proposed for automatically recognizing and segmenting multiple organ regions on three-dimensional (3D) computed tomography (CT) images. CT images are widely used to visualize 3D anatomi...
Advances in experimental medicine and biology
Jan 1, 2020
This chapter proposes a method to detect metastatic liver cancer from X-ray CT images using a convolutional neural network (CNN). The proposed method generates various lesion images by the combination of three kinds of generation methods: (1) synthes...
BACKGROUND: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Comput...
PURPOSE: X-ray pelvimetry is typically performed for the diagnosis of the cephalopelvic disproportion (CPD). The purpose of this study was to assess the utility of new computed tomography (CT) reconstruction "deep learning based reconstruction (DLR) ...
OBJECTIVE: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images.
Diagnostic and interventional radiology (Ankara, Turkey)
Jan 1, 2020
PURPOSE: To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver trans...
OBJECTIVES: Noise, commonly encountered on computed tomography (CT) images, can impact diagnostic accuracy. To reduce the image noise, we developed a deep-learning reconstruction (DLR) method that integrates deep convolutional neural networks into im...
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2020
Computed tomographic (CT) is a fundamental imaging modality to generate cross-sectional views of internal anatomy in a living subject or interrogate material composition of an object, and it has been routinely used in clinical applications and nondes...
Journal of computer assisted tomography
Jan 1, 2020
Deep learning (DL), part of a broader family of machine learning methods, is based on learning data representations rather than task-specific algorithms. Deep learning can be used to improve the image quality of clinical scans with image noise reduct...
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