Objective: Accurate segmentation and partitioning of lesions in PET images provide computer-aided procedures and doctors with parameters for tumour diagnosis, staging and prognosis. Currently, PET segmentation and lesion partitioning are manually measured by radiologists, which is time consuming and laborious, and tedious manual procedures might lead to inaccurate measurement results. Therefore, we designed a new automatic multiprocessing scheme for PET image pre-screening, noise reduction, segmentation and lesion partitioning in this study. PET image pre-screening can reduce the time cost of noise reduction, segmentation and lesion partitioning methods, and denoising can enhance both quantitative metrics and visual quality for better segmentation accuracy. For pre-screening, we propose a new differential activation filter (DAF) to screen the lesion images from whole-body scanning. For noise reduction, neural network inverse (NN inverse) as the inverse transformation of generalized Anscombe transformation (GAT), which does not depend on the distribution of residual noise, was presented to improve the SNR of images. For segmentation and lesion partitioning, definition density peak clustering (DDPC) was proposed to realize instance segmentation of lesion and normal tissue with unsupervised images, which helped reduce the cost of density calculation and completely deleted the cluster halo. The experimental results of clinical data demonstrate that our proposed methods have good results and better performance in noise reduction, segmentation and lesion partitioning compared with state-of-the-art methods.

中文翻译：

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一种用于 PET 图像预筛选、降噪、分割和病灶划分的多处理方案

客观的：PET 图像中病变的准确分割和分区为计算机辅助程序和医生提供了用于肿瘤诊断、分期和预后的参数。目前，PET分割和病灶划分由放射科医师手动测量，费时费力，繁琐的手动程序可能导致测量结果不准确。因此，我们设计了一种新的自动多处理方案，用于本研究中的 PET 图像预筛选、降噪、分割和病灶分区。PET 图像预筛选可以减少降噪、分割和病灶划分方法的时间成本，去噪可以同时增强定量指标和视觉质量，以获得更好的分割精度。对于预审，我们提出了一种新的差分激活滤波器（DAF）来筛选全身扫描的病变图像。对于降噪，神经网络逆（NN inverse）作为广义安斯科姆变换（GAT）的逆变换，不依赖于残余噪声的分布，被提出来提高图像的信噪比。在分割和病灶划分方面，提出了定义密度峰值聚类（DDPC），利用无监督图像实现病灶和正常组织的实例分割，降低了密度计算的成本，彻底消除了簇晕。临床数据的实验结果表明，与最先进的方法相比，我们提出的方法在降噪、分割和病灶划分方面具有良好的效果和更好的性能。