Shin Seung Yeon, Shen Thomas C, Summers Ronald M
ArXiv. 2023 Jul 11:arXiv:2307.05804v1.
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.
我们提出了一种在训练分割和检测网络时纳入CT扫描上目标病变强度信息的方法。我们首先根据目标病变的强度直方图构建基于强度的病变概率（ILP）函数。它用于根据每个体素的强度计算其为病变的概率。最后，为每个输入CT扫描计算得到的ILP图作为网络训练的额外监督信息提供，其目的是以不增加标注成本的方式，根据强度值告知网络可能的病变位置。该方法被应用于改善三种不同病变类型的分割，即小肠类癌肿瘤、肾肿瘤和肺结节。还研究了所提方法在检测任务上的有效性。就每个病例的Dice分数而言，我们观察到在分割小肠类癌肿瘤、肾肿瘤和肺结节时分别有41.3% -> 47.8%、74.2% -> 76.0%和26.4% -> 32.7%的提升。在检测肾肿瘤时，平均精度方面实现了64.6% -> 75.5%的提升。还展示了ILP图不同用法的结果以及不同数量训练数据的影响。
