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Med Image Anal ; 61: 101654, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-32066065


Objective and quantitative assessment of fundus image quality is essential for the diagnosis of retinal diseases. The major factors in fundus image quality assessment are image artifact, clarity, and field definition. Unfortunately, most of existing quality assessment methods focus on the quality of overall image, without interpretable quality feedback for real-time adjustment. Furthermore, these models are often sensitive to the specific imaging devices, and cannot generalize well under different imaging conditions. This paper presents a new multi-task domain adaptation framework to automatically assess fundus image quality. The proposed framework provides interpretable quality assessment with both quantitative scores and quality visualization for potential real-time image recapture with proper adjustment. In particular, the present approach can detect optic disc and fovea structures as landmarks, to assist the assessment through coarse-to-fine feature encoding. The framework also exploit semi-tied adversarial discriminative domain adaptation to make the model generalizable across different data sources. Experimental results demonstrated that the proposed algorithm outperforms different state-of-the-art approaches and achieves an area under the ROC curve of 0.9455 for the overall quality classification.

Front Neurol ; 10: 647, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31297079


Computed Tomography Perfusion (CTP) imaging is a cost-effective and fast approach to provide diagnostic images for acute stroke treatment. Its cine scanning mode allows the visualization of anatomic brain structures and blood flow; however, it requires contrast agent injection and continuous CT scanning over an extended time. In fact, the accumulative radiation dose to patients will increase health risks such as skin irritation, hair loss, cataract formation, and even cancer. Solutions for reducing radiation exposure include reducing the tube current and/or shortening the X-ray radiation exposure time. However, images scanned at lower tube currents are usually accompanied by higher levels of noise and artifacts. On the other hand, shorter X-ray radiation exposure time with longer scanning intervals will lead to image information that is insufficient to capture the blood flow dynamics between frames. Thus, it is critical for us to seek a solution that can preserve the image quality when the tube current and the temporal frequency are both low. We propose STIR-Net in this paper, an end-to-end spatial-temporal convolutional neural network structure, which exploits multi-directional automatic feature extraction and image reconstruction schema to recover high-quality CT slices effectively. With the inputs of low-dose and low-resolution patches at different cross-sections of the spatio-temporal data, STIR-Net blends the features from both spatial and temporal domains to reconstruct high-quality CT volumes. In this study, we finalize extensive experiments to appraise the image restoration performance at different levels of tube current and spatial and temporal resolution scales.The results demonstrate the capability of our STIR-Net to restore high-quality scans at as low as 11% of absorbed radiation dose of the current imaging protocol, yielding an average of 10% improvement for perfusion maps compared to the patch-based log likelihood method.