CRDet: A circle representation detector for lung granulomas based on multi-scale attention features with center point calibration

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL Computerized Medical Imaging and Graphics Pub Date : 2024-02-07 DOI:10.1016/j.compmedimag.2024.102354
Yu Jin , Juan Liu , Yuanyuan Zhou , Rong Chen , Hua Chen , Wensi Duan , Yuqi Chen , Xiao-Lian Zhang
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Abstract

Lung granuloma is a very common lung disease, and its specific diagnosis is important for determining the exact cause of the disease as well as the prognosis of the patient. And, an effective lung granuloma detection model based on computer-aided diagnostics (CAD) can help pathologists to localize granulomas, thereby improving the efficiency of the specific diagnosis. However, for lung granuloma detection models based on CAD, the significant size differences between granulomas and how to better utilize the morphological features of granulomas are both critical challenges to be addressed. In this paper, we propose an automatic method CRDet to localize granulomas in histopathological images and deal with these challenges. We first introduce the multi-scale feature extraction network with self-attention to extract features at different scales at the same time. Then, the features will be converted to circle representations of granulomas by circle representation detection heads to achieve the alignment of features and ground truth. In this way, we can also more effectively use the circular morphological features of granulomas. Finally, we propose a center point calibration method at the inference stage to further optimize the circle representation. For model evaluation, we built a lung granuloma circle representation dataset named LGCR, including 288 images from 50 subjects. Our method yielded 0.316 mAP and 0.571 mAR, outperforming the state-of-the-art object detection methods on our proposed LGCR.

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CRDet:基于多尺度注意力特征和中心点校准的肺肉芽肿圆表示检测器。
肺肉芽肿是一种非常常见的肺部疾病,其具体诊断对于确定疾病的确切病因和患者的预后非常重要。而基于计算机辅助诊断(CAD)的有效肺肉芽肿检测模型可以帮助病理学家定位肉芽肿,从而提高具体诊断的效率。然而,对于基于 CAD 的肺肉芽肿检测模型而言,肉芽肿之间的显著大小差异以及如何更好地利用肉芽肿的形态特征都是亟待解决的难题。本文提出了一种在组织病理学图像中定位肉芽肿的自动方法 CRDet,以应对这些挑战。我们首先引入了具有自我关注功能的多尺度特征提取网络,以同时提取不同尺度的特征。然后,通过圆表示检测头将特征转换为肉芽肿的圆表示,实现特征与地面实况的对齐。这样,我们也能更有效地利用肉芽肿的圆形形态特征。最后,我们在推理阶段提出了一种中心点校准方法,以进一步优化圆形表示。为了对模型进行评估,我们建立了一个名为 LGCR 的肺肉芽肿圆形表示数据集,其中包括来自 50 名受试者的 288 幅图像。我们的方法得到了 0.316 mAP 和 0.571 mAR,在我们提出的 LGCR 上优于最先进的物体检测方法。
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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
期刊介绍: The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.
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