Deep learning methods, especially convolutional neural networks, have been successfully applied to lesion segmentation in breast ultrasound (BUS) images. However, pattern complexity and intensity similarity between the surrounding tissues (i.e., background) and lesion regions (i.e., foreground) bring challenges for lesion segmentation. Considering that such rich texture information is contained in background, very few methods have tried to explore and exploit background-salient representations for assisting foreground segmentation. Additionally, other characteristics of BUS images, i.e., 1) low-contrast appearance and blurry boundary, and 2) significant shape and position variation of lesions, also increase the difficulty in accurate lesion segmentation. In this paper, we present a saliency-guided morphology-aware U-Net (SMU-Net) for lesion segmentation in BUS images. The SMU-Net is composed of a main network with an additional middle stream and an auxiliary network. Specifically, we first propose generation of saliency maps which incorporate both low-level and high-level image structures, for foreground and background. These saliency maps are then employed to guide the main network and auxiliary network for respectively learning foreground-salient and background-salient representations. Furthermore, we devise an additional middle stream which basically consists of background-assisted fusion, shape-aware, edge-aware and position-aware units. This stream receives the coarse-to-fine representations from the main network and auxiliary network for efficiently fusing the foreground-salient and background-salient features and enhancing the ability of learning morphological information for network. Extensive experiments on five datasets demonstrate higher performance and superior robustness to the scale of dataset than several state-of-the-art deep learning approaches in breast lesion segmentation in ultrasound image.

The fusion of multi-modal data (e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET)) has been prevalent for accurate identification of Alzheimer's disease (AD) by providing complementary structural and functional information. However, most of the existing methods simply concatenate multi-modal features in the original space and ignore their underlying associations which may provide more discriminative characteristics for AD identification. Meanwhile, how to overcome the overfitting issue caused by high-dimensional multi-modal data remains appealing. To this end, we propose a relation-induced multi-modal shared representation learning method for AD diagnosis. The proposed method integrates representation learning, dimension reduction, and classifier modeling into a unified framework. Specifically, the framework first obtains multi-modal shared representations by learning a bi-directional mapping between original space and shared space. Within this shared space, we utilize several relational regularizers (including feature-feature, feature-label, and sample-sample regularizers) and auxiliary regularizers to encourage learning underlying associations inherent in multi-modal data and alleviate overfitting, respectively. Next, we project the shared representations into the target space for AD diagnosis. To validate the effectiveness of our proposed approach, we conduct extensive experiments on two independent datasets (i.e., ADNI-1 and ADNI-2), and the experimental results demonstrate that our proposed method outperforms several state-of-the-art methods.

Repeated X-ray computed tomography (CT) scans are often required in several specific applications such as perfusion imaging, image-guided biopsy needle, image-guided intervention, and radiotherapy with noticeable benefits. However, the associated cumulative radiation dose significantly increases as comparison with that used in the conventional CT scan, which has raised major concerns in patients. In this study, to realize radiation dose reduction by reducing the X-ray tube current and exposure time (mAs) in repeated CT scans, we propose a prior-image induced nonlocal (PINL) regularization for statistical iterative reconstruction via the penalized weighted least-squares (PWLS) criteria, which we refer to as “PWLS-PINL”. Specifically, the PINL regularization utilizes the redundant information in the prior image and the weighted least-squares term considers a data-dependent variance estimation, aiming to improve current low-dose image quality. Subsequently, a modified iterative successive overrelaxation algorithm is adopted to optimize the associative objective function. Experimental results on both phantom and patient data show that the present PWLS-PINL method can achieve promising gains over the other existing methods in terms of the noise reduction, low-contrast object detection, and edge detail preservation.