Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, 20892, MD, USA.
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, 20892, MD, USA.
Comput Med Imaging Graph. 2023 Sep;108:102259. doi: 10.1016/j.compmedimag.2023.102259. Epub 2023 Jun 13.
We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks. We first build an intensity-based lesion probability (ILP) function from an intensity histogram of the target lesion. It is used to compute the probability of being the lesion for each voxel based on its intensity. Finally, the computed ILP map of each input CT scan is provided as additional supervision for network training, which aims to inform the network about possible lesion locations in terms of intensity values at no additional labeling cost. The method was applied to improve the segmentation of three different lesion types, namely, small bowel carcinoid tumor, kidney tumor, and lung nodule. The effectiveness of the proposed method on a detection task was also investigated. We observed improvements of 41.3% → 47.8%, 74.2% → 76.0%, and 26.4% → 32.7% in segmenting small bowel carcinoid tumor, kidney tumor, and lung nodule, respectively, in terms of per case Dice scores. An improvement of 64.6% → 75.5% was achieved in detecting kidney tumors in terms of average precision. The results of different usages of the ILP map and the effect of varied amount of training data are also presented.
我们提出了一种方法，将 CT 扫描中目标病变的强度信息纳入分割和检测网络的训练中。我们首先从目标病变的强度直方图构建基于强度的病变概率（ILP）函数。它用于根据其强度计算每个体素成为病变的概率。最后，为每个输入 CT 扫描计算的计算 ILP 图作为网络训练的额外监督，旨在根据强度值告知网络可能的病变位置，而无需额外的标记成本。该方法应用于提高三种不同病变类型（即小肠类癌肿瘤、肾肿瘤和肺结节）的分割效果。还研究了该方法在检测任务中的有效性。我们观察到，在小肠类癌肿瘤、肾肿瘤和肺结节的分割方面，分别将每个病例的 Dice 评分提高了 41.3%→47.8%、74.2%→76.0%和 26.4%→32.7%。在检测肾肿瘤方面，平均精度提高了 64.6%→75.5%。还展示了不同用途的 ILP 图的结果以及不同数量的训练数据的影响。
