343 research outputs found
Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion
This paper aims to solve a fundamental problem in intensity-based 2D/3D
registration, which concerns the limited capture range and need for very good
initialization of state-of-the-art image registration methods. We propose a
regression approach that learns to predict rotation and translations of
arbitrary 2D image slices from 3D volumes, with respect to a learned canonical
atlas co-ordinate system. To this end, we utilize Convolutional Neural Networks
(CNNs) to learn the highly complex regression function that maps 2D image
slices into their correct position and orientation in 3D space. Our approach is
attractive in challenging imaging scenarios, where significant subject motion
complicates reconstruction performance of 3D volumes from 2D slice data. We
extensively evaluate the effectiveness of our approach quantitatively on
simulated MRI brain data with extreme random motion. We further demonstrate
qualitative results on fetal MRI where our method is integrated into a full
reconstruction and motion compensation pipeline. With our CNN regression
approach we obtain an average prediction error of 7mm on simulated data, and
convincing reconstruction quality of images of very young fetuses where
previous methods fail. We further discuss applications to Computed Tomography
and X-ray projections. Our approach is a general solution to the 2D/3D
initialization problem. It is computationally efficient, with prediction times
per slice of a few milliseconds, making it suitable for real-time scenarios.Comment: 8 pages, 4 figures, 6 pages supplemental material, currently under
review for MICCAI 201
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of
medical image analysis. However, despite these achievements, the further
enhancement of deep learning models for medical image analysis faces a
significant challenge due to the scarcity of large, well-annotated datasets. To
address this issue, recent years have witnessed a growing emphasis on the
development of data-efficient deep learning methods. This paper conducts a
thorough review of data-efficient deep learning methods for medical image
analysis. To this end, we categorize these methods based on the level of
supervision they rely on, encompassing categories such as no supervision,
inexact supervision, incomplete supervision, inaccurate supervision, and only
limited supervision. We further divide these categories into finer
subcategories. For example, we categorize inexact supervision into multiple
instance learning and learning with weak annotations. Similarly, we categorize
incomplete supervision into semi-supervised learning, active learning, and
domain-adaptive learning and so on. Furthermore, we systematically summarize
commonly used datasets for data efficient deep learning in medical image
analysis and investigate future research directions to conclude this survey.Comment: Under Revie
Learning Algorithms for Fat Quantification and Tumor Characterization
Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice
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