Volume 38, Issue 10

Medical image segmentation is an important step in medical image analysis. With the rapid development of a convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, blood vessel detection, lung segmentation, cell segmentation, and so on. Previously, U-net based approaches have been proposed. However, the consecutive pooling and strided convolutional operations led to the loss of some spatial information. In this paper, we propose a context encoder network (CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation. CE-Net mainly contains three major components: a feature encoder module, a context extractor, and a feature decoder module. We use the pretrained ResNet block as the fixed feature extractor. The context extractor module is formed by a newly proposed dense atrous convolution block and a residual multi-kernel pooling block. We applied the proposed CE-Net to different 2D medical image segmentation tasks. Comprehensive results show that the proposed method outperforms the original U-Net method and other state-of-the-art methods for optic disc segmentation, vessel detection, lung segmentation, cell contour segmentation, and retinal optical coherence tomography layer segmentation.

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Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, the scan time limitations may prohibit the acquisition of certain contrasts, and some contrasts may be corrupted by noise and artifacts. In such cases, the ability to synthesize unacquired or corrupted contrasts can improve diagnostic utility. For m...

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The fusion of complementary information contained in multi-modality data [e.g., magnetic resonance imaging (MRI), positron emission tomography (PET), and genetic data] has advanced the progress of automated Alzheimer’s disease (AD) diagnosis. However, multi-modality based AD diagnostic models are often hindered by the missing data, i.e., not all the subjects have complete multi-modality data. One ...

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Computed tomography (CT) is widely used in medical diagnosis and non-destructive detection. Image reconstruction in CT aims to accurately recover pixel values from measured line integrals, i.e., the summed pixel values along straight lines. Provided that the acquired data satisfy the data sufficiency condition as well as other conditions regarding the view angle sampling interval and the severity ...

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Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that can simultaneously measure multiple important tissue properties of human body. Although MRF has demonstrated improved scan efficiency as compared to conventional techniques, further acceleration is still desired for translation into routine clinical practice. The purpose of this paper is to accelerate MRF acquisition ...

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Obtaining the complete segmentation map of retinal lesions is the first step toward an automated diagnosis tool for retinopathy that is interpretable in its decision-making. However, the limited availability of ground truth lesion detection maps at a pixel level restricts the ability of deep segmentation neural networks to generalize over large databases. In this paper, we propose a novel approach...

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A major challenge in the field of segmentation in digital pathology is given by the high effort for manual data annotations in combination with many sources introducing variability in the image domain. This requires methods that are able to cope with variability without requiring to annotate a large amount of samples for each characteristic. In this paper, we develop approaches based on adversaria...

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Magnetic particle imaging (MPI) is a promising new tracer-based imaging modality. The steady-state, nonlinear magnetization physics most fundamental to MPI typically predicts improving resolution with increasing tracer magnetic core size. For larger tracers, and given typical excitation slew rates, this steady-state prediction is compromised by dynamic processes that induce a significant secondary...

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A statistical shape-constrained reconstruction (SSCR) framework is presented to incorporate the statistical prior information of human lung shapes for lung electrical impedance tomography. The prior information is extracted from 8000 chest-computed tomography scans across 800 patients. The reconstruction framework is implemented with two approaches—a one-step SSCR and an iterative SSCR in lung ima...

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The ability to perceive and regulate emotion is a key component of cognition that is often disrupted by disease. Current neuroimaging studies regarding emotion regulation have implicated a number of cortical regions and identified several EEG features of interest, including the late positive potential and frontal asymmetry. Unfortunately, currently applied methods generally lack in the resolution ...

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It is widely accepted that the optimization of medical imaging system performance should be guided by task-based measures of image quality (IQ). Task-based measures of IQ quantify the ability of an observer to perform a specific task, such as detection or estimation of a signal (e.g., a tumor). For binary signal detection tasks, the Bayesian Ideal Observer (IO) sets an upper limit of observer perf...

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Magnetic resonance fingerprinting (MRF) is able to estimate multiple quantitative tissue parameters from a relatively short acquisition. The main characteristic of an MRF sequence is the simultaneous application of 1) transient states excitation and 2) highly undersampled ${k}$ -space. Despite the promising empirical results obtained with MRF, no work has appeared that formally describes the comb...

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Hippocampus segmentation plays a significant role in mental disease diagnoses, such as Alzheimer's disease, epilepsy, and so on. Patch-based multi-atlas segmentation (PBMAS) approach is a popular method for hippocampus segmentation and has achieved a promising result. However, the PBMAS approach needs high computation cost due to registration and the segmentation accuracy is subject to the registr...

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Adequate medical images are often indispensable in contemporary deep learning-based medical imaging studies, although the acquisition of certain image modalities may be limited due to several issues including high costs and patients issues. However, thanks to recent advances in deep learning techniques, the above tough problem can be substantially alleviated by medical images synthesis, by which v...

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Conducting an accurate motion correction of liver dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging remains challenging because of intensity variations caused by contrast agents. Such variations lead to the failure of the traditional intensity-based registration method. To address this problem, we propose a correlation-weighted sparse representation framework to separate the contrast a...

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Stochastic gradient descent (SGD) is commonly used to solve (parametric) image registration problems. In the case of badly scaled problems, SGD, however, only exhibits sublinear convergence properties. In this paper, we propose an efficient preconditioner estimation method to improve the convergence rate of SGD. Based on the observed distribution of voxel displacements in the registration, we esti...

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Three-dimensional freehand imaging techniques are gaining wider adoption due to their flexibility and cost efficiency. Typical examples for such a combination of a tracking system with an imaging device are freehand SPECT or freehand 3D ultrasound. However, the quality of the resulting image data is heavily dependent on the skill of the human operator and on the level of noise of the tracking data...

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Non-rigid registration of cell nuclei in time-lapse microscopy images can be achieved through estimating the deformation fields using optical flow methods. In contrast to local optical flow models employed in the existing non-rigid registration methods, we introduce approaches based on a global optical flow model. Our registration model consists of a data fidelity term and a regularization term. W...

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In optoacoustic tomography, negatively focused detectors have been shown to improve the tangential image resolution without sacrificing sensitivity. Since no exact inversion formulas exist for optoacoustic image reconstruction with negatively focused detectors, image reconstruction in such cases is based on using the virtual-detector approximation, in which it is assumed that the response of the n...

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We introduce a local manifold regularization approach to recover dynamic MRI data from highly undersampled measurements. The proposed scheme relies on the manifold structure of local image patches at the same spatial location in a free-breathing cardiac MRI dataset; this approach is a generalization of the SmooThness Regularization on Manifolds (SToRM) scheme that exploits the global manifold stru...

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