BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging.
BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging.
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DOI：
10.3390/bioengineering10121372
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发表时间：
2023-11-29
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影响因子：
--
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来自小物体的基于医学成像的生物标志物（例如，细胞核）在医学应用中起着至关重要的作用。然而，检测和分割小对象（a.k.a.斑点）仍然是一个具有挑战性的任务。在这项研究中，我们提出了一种新的三维小斑点检测器称为BlobCUT。BlobCUT是一种不成对的图像到图像（I2 I）翻译模型，福尔斯对比不成对翻译范式。它采用斑点合成模块来生成具有相应掩模的合成3D斑点。这被纳入到迭代模型训练中作为基础事实。I2 I转换过程被设计为具有两个约束：（1）依赖于Hessian分析以保持几何性质的凸性一致性约束，以及（2）基于Kullback-Leibler发散以保持斑点的强度分布的强度分布一致性约束。BlobCUT从目标噪声斑点图像中学习固有噪声分布，并执行从噪声域到干净域的图像转换，有效地用作去噪过程以支持斑点识别。为了验证BlobCUT的性能，我们在斑点的3D模拟数据集和小鼠肾脏的3D MRI数据集上对其进行了评估。我们进行了比较分析，涉及六个国家的最先进的方法。我们的研究结果表明，BlobCUT具有上级性能和训练效率，仅利用了最先进的BlobDetGAN所需的训练时间的56.6%。这强调了BlobCUT在准确分割小斑点方面的有效性，同时在训练效率方面取得了显着的进步。
Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a novel 3D small blob detector called BlobCUT. BlobCUT is an unpaired image-to-image (I2I) translation model that falls under the Contrastive Unpaired Translation paradigm. It employs a blob synthesis module to generate synthetic 3D blobs with corresponding masks. This is incorporated into the iterative model training as the ground truth. The I2I translation process is designed with two constraints: (1) a convexity consistency constraint that relies on Hessian analysis to preserve the geometric properties and (2) an intensity distribution consistency constraint based on Kullback-Leibler divergence to preserve the intensity distribution of blobs. BlobCUT learns the inherent noise distribution from the target noisy blob images and performs image translation from the noisy domain to the clean domain, effectively functioning as a denoising process to support blob identification. To validate the performance of BlobCUT, we evaluate it on a 3D simulated dataset of blobs and a 3D MRI dataset of mouse kidneys. We conduct a comparative analysis involving six state-of-the-art methods. Our findings reveal that BlobCUT exhibits superior performance and training efficiency, utilizing only 56.6% of the training time required by the state-of-the-art BlobDetGAN. This underscores the effectiveness of BlobCUT in accurately segmenting small blobs while achieving notable gains in training efficiency.
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DOI：
10.1152/ajprenal.00044.2011
发表时间：
2011-06-01
影响因子：
4.2
作者：
Beeman, Scott C.;Zhang, Min;Bennett, Kevin M.
通讯作者：
Bennett, Kevin M.
影响因子：
8.5
作者：
Al-Hindawi, Firas;Soori, Tejaswi;Sun, Ying
通讯作者：
Sun, Ying
影响因子：
10.6
作者：
Gupta, Harshit;Jin, Kyong Hwan;Unser, Michael
通讯作者：
Unser, Michael
影响因子：
3.3
作者：
DeCost, Brian L.;Holm, Elizabeth A.
通讯作者：
Holm, Elizabeth A.
影响因子：
10.6
作者：
Dolz, Jose;Gopinath, Karthik;Ben Ayed, Ismail
通讯作者：
Ben Ayed, Ismail