In clinical practice, radiologists are reliant on the lesion size when distinguishing metastatic from non-metastatic lesions. A prerequisite for lesion sizing is their detection, as it promotes the downstream assessment of tumor spread. However, lesions vary in their size and appearance in CT scans, and radiologists often miss small lesions during a busy clinical day. To overcome these challenges, we propose the use of state-of-the-art detection neural networks to flag suspicious lesions present in the NIH DeepLesion dataset for sizing. Additionally, we incorporate a bounding box fusion technique to minimize false positives (FP) and improve detection accuracy. Finally, to resemble clinical usage, we constructed an ensemble of the best detection models to localize lesions for sizing with a precision of 65.17% and sensitivity of 91.67% at 4 FP per image. Our results improve upon or maintain the performance of current state-of-the-art methods for lesion detection in challenging CT scans.
翻译:在临床实践中,放射科医生在区分转移和非转移性损伤时,依靠损伤大小来区分转移性与非转移性损伤。损伤缩放的一个先决条件是检测它们,因为它能促进下游对肿瘤扩散的评估。然而,损伤在CT扫描中的大小和外观各不相同,放射科医生在繁忙的临床日子里往往会忽略小损伤。为了克服这些挑战,我们建议使用最新水平的检测神经网络来显示NIH深层疏松数据集中存在的可疑损伤。此外,我们采用了捆绑盒聚合技术来尽量减少假阳性并提高检测准确性。最后,为了与临床使用相似,我们制作了最佳检测模型的集合体,以精确度65.17%和灵敏度91.67%,为每张4 FP。我们在挑战CT扫描中改进或保持当前最新水平的病情检测方法的性能。