在磁共振图像上使用U-网结构自动分割von Hippel-Lindau综合征患者的透明细胞肾细胞肿瘤、肾脏和囊肿
Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images
摘要：
我们展示了在磁共振成像（MRI）上使用卷积神经网络（CNN）对von Hippel-Lindau（VHL）综合征患者的透明细胞肾细胞癌（ccRCC）、囊肿和周围正常肾实质的自动分割。从2015年到2021年，我们查询了115名VHL患者和117次扫描（3名患者进行了两次单独扫描），其中504例ccRCC和1171例囊肿。病变在T1排泄期手动分割，在所有对比增强T1序列上共配准，并用于训练2D和3D U-Net。在队列的10个随机分组上评价了U-Net性能。采用骰子相似系数（DSC）对模型进行评价。我们的2D U-Net实现了平均ccRCC病变检测曲线下面积（AUC）为0.88，肾脏、囊肿和肿瘤分割的DSC评分分别为0.78、0.40和0.46。我们的3D U-Net实现了肾脏、囊肿和肿瘤的平均ccRCC病变检测AUC为0.79，DSC评分分别为0.67、0.32和0.34。我们在MRI上使用U-Net对ccRCC进行了良好的检测和中等分割结果。正常肾实质、囊肿和肿块的自动检测和分割可以帮助放射科医生量化VHL患者的疾病负担。
英文摘要：
We demonstrate automated segmentation of clear cell renal cell carcinomas (ccRCC), cysts, and surrounding normal kidney parenchyma in patients with von Hippel-Lindau (VHL) syndrome using convolutional neural networks (CNN) on Magnetic Resonance Imaging (MRI). We queried 115 VHL patients and 117 scans (3 patients have two separate scans) with 504 ccRCCs and 1171 cysts from 2015 to 2021. Lesions were manually segmented on T1 excretory phase, co-registered on all contrast-enhanced T1 sequences and used to train 2D and 3D U-Net. The U-Net performance was evaluated on 10 randomized splits of the cohort. The models were evaluated using the dice similarity coefficient (DSC). Our 2D U-Net achieved an average ccRCC lesion detection Area under the curve (AUC) of 0.88 and DSC scores of 0.78, 0.40, and 0.46 for segmentation of the kidney, cysts, and tumors, respectively. Our 3D U-Net achieved an average ccRCC lesion detection AUC of 0.79 and DSC scores of 0.67, 0.32, and 0.34 for kidney, cysts, and tumors, respectively. We demonstrated good detection and moderate segmentation results using U-Net for ccRCC on MRI. Automatic detection and segmentation of normal renal parenchyma, cysts, and masses may assist radiologists in quantifying the burden of disease in patients with VHL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
