Read by QxMD icon Read

Medical Image Computing and Computer-assisted Intervention: MICCAI ...

Miaoyun Zhao, Li Wang, Jiawei Chen, Dong Nie, Yulai Cong, Sahar Ahmad, Angela Ho, Peng Yuan, Steve H Fung, Hannah H Deng, James Xia, Dinggang Shen
Automatic segmentation of medical images finds abundant applications in clinical studies. Computed Tomography (CT) imaging plays a critical role in diagnostic and surgical planning of craniomaxillofacial (CMF) surgeries as it shows clear bony structures. However, CT imaging poses radiation risks for the subjects being scanned. Alternatively, Magnetic Resonance Imaging (MRI) is considered to be safe and provides good visualization of the soft tissues, but the bony structures appear invisible from MRI. Therefore, the segmentation of bony structures from MRI is quite challenging...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Li Wang, Gang Li, Feng Shi, Xiaohuan Cao, Chunfeng Lian, Dong Nie, Mingxia Liu, Han Zhang, Guannan Li, Zhengwang Wu, Weili Lin, Dinggang Shen
Autism spectrum disorder (ASD) is mainly diagnosed by the observation of core behavioral symptoms. Due to the absence of early biomarkers to detect infants either with or at-risk of ASD during the first postnatal year of life, diagnosis must rely on behavioral observations long after birth. As a result, the window of opportunity for effective intervention may have passed when the disorder is detected. Therefore, it is clinically urgent to identify imaging-based biomarkers for early diagnosis and intervention...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Hongming Li, Xiaofeng Zhu, Yong Fan
We present a deep semi-nonnegative matrix factorization method for identifying subject-specific functional networks (FNs) at multiple spatial scales with a hierarchical organization from resting state fMRI data. Our method is built upon a deep semi-nonnegative matrix factorization framework to jointly detect the FNs at multiple scales with a hierarchical organization, enhanced by group sparsity regularization that helps identify subject-specific FNs without loss of inter-subject comparability. The proposed method has been validated for predicting subject-specific functional activations based on functional connectivity measures of the hierarchical multi-scale FNs of the same subjects...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
Cortical thickness analysis of brain magnetic resonance images is an important technique in neuroimaging research. There are two main computational paradigms, namely voxel-based and surface-based methods. Recently, a tetrahedron-based volumetric morphometry (TBVM) approach involving proper discretization methods was proposed. The multi-scale and physics-based geometric features generated through such methods may yield stronger statistical power. However, several challenges, such as the lack of well-defined thickness statistics and the difficulty in filling tetrahedrons into the thin and curvy cortex structure, impede the broad application of TBVM...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Hongming Li, Yong Fan
Decoding brain functional states underlying different cognitive processes using multivariate pattern recognition techniques has attracted increasing interests in brain imaging studies. Promising performance has been achieved using brain functional connectivity or brain activation signatures for a variety of brain decoding tasks. However, most of existing studies have built decoding models upon features extracted from imaging data at individual time points or temporal windows with a fixed interval, which might not be optimal across different cognitive processes due to varying temporal durations and dependency of different cognitive processes...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Hongming Li, Yong Fan
Dynamic functional connectivity analysis provides valuable information for understanding brain functional activity underlying different cognitive processes. Besides sliding window based approaches, a variety of methods have been developed to automatically split the entire functional MRI scan into segments by detecting change points of functional signals to facilitate better characterization of temporally dynamic functional connectivity patterns. However, these methods are based on certain assumptions for the functional signals, such as Gaussian distribution, which are not necessarily suitable for the fMRI data...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Jue Jiang, Yu-Chi Hu, Neelam Tyagi, Pengpeng Zhang, Andreas Rimner, Gig S Mageras, Joseph O Deasy, Harini Veeraraghavan
We present an adversarial domain adaptation based deep learning approach for automatic tumor segmentation from T2-weighted MRI. Our approach is composed of two steps: (i) a tumor-aware unsupervised cross-domain adaptation (CT to MRI), followed by (ii) semi-supervised tumor segmentation using Unet trained with synthesized and limited number of original MRIs. We introduced a novel target specific loss, called tumor-aware loss, for unsupervised cross-domain adaptation that helps to preserve tumors on synthesized MRIs produced from CT images...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Wen Zhang, Kai Shu, Suhang Wang, Huan Liu, Yalin Wang
In recent years, brain network analysis has attracted considerable interests in the field of neuroimaging analysis. It plays a vital role in understanding biologically fundamental mechanisms of human brains. As the upward trend of multi-source in neuroimaging data collection, effective learning from the different types of data sources, e.g. multimodal and longitudinal data, is much in demand. In this paper, we propose a general coupling framework, the multimodal neuroimaging network fusion with longitudinal couplings ( MMLC ), to learn the latent representations of brain networks...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Ehsan Adeli, Yu Meng, Gang Li, Weili Lin, Dinggang Shen
Studies involving dynamic infant brain development has received increasing attention in the past few years. For such studies, a complete longitudinal dataset is often required to precisely chart the early brain developmental trajectories. Whereas, in practice, we often face missing data at different time point(s) for different subjects. In this paper, we propose a new method for prediction of infant brain development scores at future time points based on longitudinal imaging measures at early time points with possible missing data...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Jianfei Liu, HaeWon Jung, Johnny Tam
Loss of cone photoreceptor neurons is a leading cause of many blinding retinal diseases. Direct visualization of these cells in the living human eye is now feasible using adaptive optics scanning light ophthalmoscopy (AOSLO). However, it remains challenging to monitor the state of specific cells across multiple visits, due to inherent eye-motion-based distortions that arise during data acquisition, artifacts when overlapping images are montaged, as well as substantial variability in the data itself. This paper presents an accurate graph matching framework that integrates (1) robust local intensity order patterns (LIOP) to describe neuron regions with illumination variation from different visits; (2) a sparse-coding based voting process to measure visual similarities of neuron pairs using LIOP descriptors; and (3) a graph matching model that combines both visual similarity and geometrical cone packing information to determine the correspondence of repeated imaging of cone photoreceptor neurons across longitudinal AOSLO datasets...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Yinghuan Shi, Wanqi Yang, Yang Gao, Dinggang Shen
Prostate segmentation, for accurate prostate localization in CT images, is regarded as a crucial yet challenging task. Nevertheless, due to the inevitable factors ( e.g. , low contrast, large appearance and shape changes), the most important problem is how to learn the informative feature representation to distinguish the prostate from non-prostate regions. We address this challenging feature learning by leveraging the manual delineation as guidance: the manual delineation does not only indicate the category of patches, but also helps enhance the appearance of prostate...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Dong Nie, Roger Trullo, Jun Lian, Caroline Petitjean, Su Ruan, Qian Wang, Dinggang Shen
Computed tomography (CT) is critical for various clinical applications, e.g., radiation treatment planning and also PET attenuation correction in MRI/PET scanner. However, CT exposes radiation during acquisition, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve radiations. Therefore, recently researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiation planning...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Yingying Zhu, Minjeong Kim, Xiaofeng Zhu, Jin Yan, Daniel Kaufer, Guorong Wu
Current learning-based methods for the diagnosis of Alzheimer's Disease (AD) rely on training a general classifier aiming to recognize abnormal structural alternations from homogenously distributed dataset deriving from a large population. However, due to diverse disease pathology, the real imaging data in routine clinic practices is highly complex and heterogeneous. Hence, prototype methods commonly performing well in the laboratory cannot achieve expected outcome when applied under the real clinic setting...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Khosro Bahrami, Islem Rekik, Feng Shi, Dinggang Shen
7T MRI scanner provides MR images with higher resolution and better contrast than 3T MR scanners. This helps many medical analysis tasks, including tissue segmentation. However, currently there is a very limited number of 7T MRI scanners worldwide. This motivates us to propose a novel image post-processing framework that can jointly generate high-resolution 7T-like images and their corresponding high-quality 7T-like tissue segmentation maps, solely from the routine 3T MR images. Our proposed framework comprises two parallel components, namely (1) reconstruction and (2) segmentation...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
John Treilhard, Susanne Smolka, Lawrence Staib, Julius Chapiro, MingDe Lin, Georgy Shakirin, James Duncan
This work addresses multi-class liver tissue classification from multi-parameter MRI in patients with hepatocellular carcinoma (HCC), and is among the first to do so. We propose a structured prediction framework to simultaneously classify parenchyma, blood vessels, viable tumor tissue, and necrosis, which overcomes limitations related to classifying these tissue classes individually and consecutively. A novel classification framework is introduced, based on the integration of multi-scale shape and appearance features to initiate the classification, which is iteratively refined by augmenting the feature space with both structured and rotationally invariant label context features...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Konstantin Dmitriev, Arie E Kaufman, Ammar A Javed, Ralph H Hruban, Elliot K Fishman, Anne Marie Lennon, Joel H Saltz
There are many different types of pancreatic cysts. These range from completely benign to malignant, and identifying the exact cyst type can be challenging in clinical practice. This work describes an automatic classification algorithm that classifies the four most common types of pancreatic cysts using computed tomography images. The proposed approach utilizes the general demographic information about a patient as well as the imaging appearance of the cyst. It is based on a Bayesian combination of the random forest classifier, which learns subclass-specific demographic, intensity, and shape features, and a new convolutional neural network that relies on the fine texture information...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Lei Chen, Han Zhang, Kim-Han Thung, Luyan Liu, Junfeng Lu, Jinsong Wu, Qian Wang, Dinggang Shen
MGMT promoter methylation and IDH1 mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which is laborious, invasive and time-consuming. Accurate presurgical prediction of their statuses based on preoperative imaging data is of great clinical value towards better treatment plan. In this paper, we propose a novel Multi-label Inductive Matrix Completion (MIMC) model, highlighted by the online inductive learning strategy, to jointly predict both MGMT and IDH1 statuses...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Mingxia Liu, Jun Zhang, Ehsan Adeli, Dinggang Shen
Jointly identifying brain diseases and predicting clinical scores have attracted increasing attention in the domain of computer-aided diagnosis using magnetic resonance imaging (MRI) data, since these two tasks are highly correlated. Although several joint learning models have been developed, most existing methods focus on using human-engineered features extracted from MRI data. Due to the possible heterogeneous property between human-engineered features and subsequent classification/regression models, those methods may lead to sub-optimal learning performance...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Geng Chen, Bin Dong, Yong Zhang, Dinggang Shen, Pew-Thian Yap
In this paper, we introduce a strategy for performing neighborhood matching on general non-Euclidean and non-flat domains. Essentially, this involves representing the domain as a graph and then extending the concept of convolution from regular grids to graphs. Acknowledging the fact that convolutions are features of local neighborhoods, neighborhood matching is carried out using the outcome of multiple convolutions at multiple scales. All these concepts are encapsulated in a sound mathematical framework, called graph framelet transforms (GFTs), which allows signals residing on non-flat domains to be decomposed according to multiple frequency subbands for rich characterization of signal patterns...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Moo K Chung, Ying Ji Chuang, Houri K Vorperian
We present a unified online statistical framework for quantifying a collection of binary images. Since medical image segmentation is often done semi-automatically, the resulting binary images may be available in a sequential manner. Further, modern medical imaging datasets are too large to fit into a computer's memory. Thus, there is a need to develop an iterative analysis framework where the final statistical maps are updated sequentially each time a new image is added to the analysis. We propose a new algorithm for online statistical inference and apply to characterize mandible growth during the first two decades of life...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"