Small Blob Detector Using Bi-Threshold Constrained Adaptive Scales.
Small Blob Detector Using Bi-Threshold Constrained Adaptive Scales.
复制标题
使用双阈值约束自适应尺度的小斑点检测器。
DOI：
10.1109/tbme.2020.3046252
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Bennett KM
中科院分区：
文献类型：
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作者：
Xu Y;Wu T;Charlton JR;Gao F;Bennett KM
医学成像技术的最新进展为医学实践带来了巨大的希望。发现成像生物标志物可为疾病诊断、预后和治疗评估提供信息。从图像中检测和分割对象通常是这些生物标志物定量测量的第一步。检测图像中的对象，特别是称为斑点的小对象的挑战包括低图像分辨率、图像噪声和斑点之间的重叠。本研究提出一种双阈值约束自适应尺度（BTCAS）斑点检测器，以揭示U-Net阈值和高斯差分（DoG）尺度之间的关系，从而推导出多阈值，多尺度的小斑点检测器。利用U-Net的概率阈值的下限和上限，在斑点中心之间渲染距离的两个二值化图。每个斑点被变换到具有自适应识别的局部最优尺度的DoG空间。Hessian凸性映射使用自适应尺度渲染，并且解决了U-Net典型的欠分割问题。为了验证所提出的BTCAS的性能，研究了斑点的3D模拟数据集（n=20）、人肾脏的3D MRI数据集和小鼠肾脏的3D MRI数据集。BTCAS与四种最先进的方法进行了比较：HDoG，具有标准阈值的U-Net，具有最佳阈值的U-Net，以及使用精度，召回率，F分数，Dice和IoU的UH-DoG。我们的结论是，BTCAS在统计上优于比较检测器。
Recent advances in medical imaging technology bring great promises for medicine practices. Imaging biomarkers are discovered to inform disease diagnosis, prognosis, and treatment assessment. Detecting and segmenting objects from images are often the first steps in quantitative measurement of these biomarkers. The challenges of detecting objects in images, particularly small objects known as blobs, include low image resolution, image noise and overlap among the blobs. This research proposes a Bi-Threshold Constrained Adaptive Scale (BTCAS) blob detector to uncover the relationship between the U-Net threshold and the Difference of Gaussian (DoG) scale to derive a multi-threshold, multi-scale small blob detector. With lower and upper bounds on the probability thresholds from U-Net, two binarized maps of the distance are rendered between blob centers. Each blob is transformed to a DoG space with an adaptively identified local optimum scale. A Hessian convexity map is rendered using the adaptive scale, and the under-segmentation typical of the U-Net is resolved. To validate the performance of the proposed BTCAS, a 3D simulated dataset (n=20) of blobs, a 3D MRI dataset of human kidneys and a 3D MRI dataset of mouse kidneys, are studied. BTCAS is compared against four state-of-the-art methods: HDoG, U-Net with standard thresholding, U-Net with optimal thresholding, and UH-DoG using precision, recall, F-score, Dice and IoU. We conclude that BTCAS statistically outperforms the compared detectors.