As a fundamental feature of solid surfaces, wettability is playing an increasingly important role in our daily life. Benefitting from the inspiration of biological paradigms and the development in manufacturing technology, numerous wettability materials with elaborately designed surface topology and chemical compositions have been fabricated. Based on these advances, wettability materials have found broad technological implications in various fields ranging from academy, industry, agriculture to biomedical engineering. Among them, the practical applications of wettability materials in biomedical‐related fields are receiving remarkable researches during the past decades because of the increasing attention to healthcare. In this review, the research progress of materials with specific wettability is discussed. After briefly introducing the underlying mechanisms, the fabrication strategies of artificial materials with specific wettability are described. The emphasis is put on the application progress of wettability biomaterials in biomedical engineering. The prospects for the future trend of wettability materials are also presented.

Zhou QQ, et al. Korean J Radiol. 2020 Jul;21(7):869-879. https://doi.org/10.3348/kjr.2019.0651

Accurate segmentation of the prostate is a key step in external beam radiation therapy treatments. In this paper, we tackle the challenging task of prostate segmentation in CT images by a two-stage network with 1) the first stage to fast localize, and 2) the second stage to accurately segment the prostate. To precisely segment the prostate in the second stage, we formulate prostate segmentation into a multi-task learning framework, which includes a main task to segment the prostate, and an auxiliary task to delineate the prostate boundary. Here, the second task is applied to provide additional guidance of unclear prostate boundary in CT images. Besides, the conventional multi-task deep networks typically share most of the parameters (i.e., feature representations) across all tasks, which may limit their data fitting ability, as the specificity of different tasks are inevitably ignored. By contrast, we solve them by a hierarchically-fused U-Net structure, namely HF-UNet. The HF-UNet has two complementary branches for two tasks, with the novel proposed attention-based task consistency learning block to communicate at each level between the two decoding branches. Therefore, HF-UNet endows the ability to learn hierarchically the shared representations for different tasks, and preserve the specificity of learned representations for different tasks simultaneously. We did extensive evaluations of the proposed method on a large planning CT image dataset and a benchmark prostate zonal dataset. The experimental results show HF-UNet outperforms the conventional multi-task network architectures and the state-of-the-art methods.

Deep grey matter structures are associated with memory and other important functions, which were typically impaired in Alzheimer’s disease (AD) and its preclinical stage of mild cognitive impairment (MCI). However, systemically characterizing the subregional atrophy and deformations in these structures in AD and MCI still need more investigations. In this paper, we combined complex volumetry- and vertex-based analysis to investigate the pattern of subregional morphological alterations in deep grey matter structures and its association with clinical assessment scores in AD (n = 30) and MCI patients (n = 30). Among all 7 pairs of structures, compared with normal controls, the bilateral hippocampi and nucleus accumbens (NAc) showed significant atrophy in AD (p 0.05). In conclusion, the degeneration of these four subregions in bilateral hippocampi and NAc resulted in severe cognition decline of patients, which might be potential target regions of treatment in preclinical AD. The surface analysis could provide additional information to volume comparison in finding the early pathological progress in deep grey matter structures.

Frontotemporal dementia (FTD) and Alzheimer’s disease (AD) have overlapping symptoms, and accurate differential diagnosis is important for targeted intervention and treatment. Previous studies suggest that the deep learning (DL) techniques have the potential to solve the differential diagnosis problem of FTD, AD and normal controls (NCs), but its performance is still unclear. In addition, existing DL-assisted diagnostic studies still rely on hypothesis-based expert-level preprocessing. On the one hand, it imposes high requirements on clinicians and data themselves; On the other hand, it hinders the backtracking of classification results to the original image data, resulting in the classification results cannot be interpreted intuitively. In the current study, a large cohort of 3D T1-weighted structural magnetic resonance imaging (MRI) volumes (n = 4,099) was collected from two publicly available databases, i.e., the ADNI and the NIFD. We trained a DL-based network directly based on raw T1 images to classify FTD, AD and corresponding NCs. And we evaluated the convergence speed, differential diagnosis ability, robustness and generalizability under nine scenarios. The proposed network yielded an accuracy of 91.83% based on the most common T1-weighted sequence [magnetization-prepared rapid acquisition with gradient echo (MPRAGE)]. The knowledge learned by the DL network through multiple classification tasks can also be used to solve subproblems, and the knowledge is generalizable and not limited to a specified dataset. Furthermore, we applied a gradient visualization algorithm based on guided backpropagation to calculate the contribution graph, which tells us intuitively why the DL-based networks make each decision. The regions making valuable contributions to FTD were more widespread in the right frontal white matter regions, while the left temporal, bilateral inferior frontal and parahippocampal regions were contributors to the classification of AD. Our results demonstrated that DL-based networks have the ability to solve the enigma of differential diagnosis of diseases without any hypothesis-based preprocessing. Moreover, they may mine the potential patterns that may be different from human clinicians, which may provide new insight into the understanding of FTD and AD.