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.

翻译：我们提出一种方法，在训练分割与检测网络时引入CT扫描中目标病灶的强度信息。首先基于目标病灶的强度直方图构建强度-病灶概率（ILP）函数，该函数可根据每个体素的强度计算其属于病灶的概率。随后将每个输入CT扫描计算所得的ILP图作为网络训练的额外监督信息，旨在以零额外标注成本向网络传递基于强度值的潜在病灶位置信息。该方法被应用于改进三种不同病灶类型的分割：小肠类癌、肾肿瘤及肺结节，同时探究了该方法在检测任务中的有效性。实验结果表明，以每个病例的Dice分数为评价指标，小肠类癌、肾肿瘤和肺结节的分割性能分别从41.3%提升至47.8%、74.2%提升至76.0%、26.4%提升至32.7%；肾肿瘤检测的平均精度从64.6%提升至75.5%。此外还报告了ILP图的不同使用方式及训练数据量变化对结果的影响。