Quantitative bone single-photon emission computed tomography (QBSPECT) has the potential to provide a better quantitative assessment of bone metastasis than planar bone scintigraphy due to its ability to better quantify activity in overlapping structures. An important element of assessing response of bone metastasis is accurate image segmentation. However, limited by the properties of QBSPECT images, the segmentation of anatomical regions-of-interests (ROIs) still relies heavily on the manual delineation by experts. This work proposes a fast and robust automated segmentation method for partitioning a QBSPECT image into lesion, bone, and background. We present a new unsupervised segmentation loss function and its semi- and supervised variants for training a convolutional neural network (ConvNet). The loss functions were developed based on the objective function of the classical Fuzzy C-means (FCM) algorithm. We conducted a comprehensive study to compare our proposed methods with ConvNets trained using supervised loss functions and conventional clustering methods. The Dice similarity coefficient (DSC) and several other metrics were used as figures of merit as applied to the task of delineating lesion and bone in both simulated and clinical SPECT/CT images. We experimentally demonstrated that the proposed methods yielded good segmentation results on a clinical dataset even though the training was done using realistic simulated images. A ConvNet-based image segmentation method that uses novel loss functions was developed and evaluated. The method can operate in unsupervised, semi-supervised, or fully-supervised modes depending on the availability of annotated training data. The results demonstrated that the proposed method provides fast and robust lesion and bone segmentation for QBSPECT/CT. The method can potentially be applied to other medical image segmentation applications.
翻译:用于评估骨质转移反应的一个重要要素是准确的图像分割。然而,由于QBSPECT图像的特性、解剖区域(ROCM)算法的分解仍然严重依赖专家的手工划界。这项工作提出了一种快速和稳健的自动分解方法,用于将QBSPECT图像分解成腐蚀性、骨质和背景。我们展示了一种新的未受监督的分解损失功能及其半和监督的变体,用于培训卷动神经网络(Convaltraismal net) 。但受QBSPECT图像特性的限制,即解剖区域(ROCM)算法的分解。我们开展了一项全面研究,将我们提出的方法与使用监管下的损失函数和常规集成方法培训的ConvalNets。Diceality coalive(DSBC) 和若干其他数据库的分解法都用于进行模拟分析,而我们使用的是用于模拟骨质化结构分析结果,而我们使用的是用于模拟结构分析分析结果的模型中的一项数据。