基于深度学习的多模态影像脑胶质母细胞瘤放疗靶区的自动勾画研究

目的 通过建立深度学习模型,探索多模态影像对脑胶质母细胞瘤放疗靶区自动勾画效果的影响。方法 收集30例脑胶质母细胞瘤患者的电子计算机断层扫描(CT)序列和磁共振成像(MRI)的对比增强T1加权序列(T1C)以及T2液体衰减反转恢复序列(T2-FLAIR),对每例病例的原发肿瘤靶区(GTV)及其对应的临床靶区1(CTV1)和临床靶区2(CTV2)根据RTOG标准进行人工勾画,并设计4种不同的数据集:CT数据集(仅含30例CT序列的单模态数据)、CT-T1C数据集(包含30例CT序列和T1C序列的双模态数据)、CT-T2-FLAIR数据集(包含30例CT序列和T2-FLAIR序列的双模态数据)和CT-MRIs数据集(包含30例CT序列、T1C序列和T2-FLAIR序列的三模态数据)。使用每种数据集中的25例对改进后的3D U-Net进行训练,并用剩余5例进行测试。评价测试样本中GTV、CTV1和CTV2的自动勾画效果,定量评估指标包括Dice相似系数(DSC),95% Hausdorff距离(HD95)和相对体积误差(RVE)。结果 该3D U-Net模型在多模态影像CT-MRIs上获得最好的GTV自动分割结果,与在单模态影像CT的自动分割结果相比(DSC: 0.94 vs. 0.79, HD95: 2.09 mm vs. 12.33 mm and RVE: 1.16% vs. 20.14%),DSC(t=3.78,P<0.05)和HD95 (t=4.07, P<0.05)的差异有统计学意义;在多模态影像CT-MRIs的CTV1和CTV2自动分割结果(DSC: 0.90 vs. 0.91, HD95: 3.78 mm vs. 2.41 mm, RVE: 3.61% vs. 5.35%)也均有较好的一致性,但与单模态影像CT的自动分割结果相比,两个靶区的DSC和HD95的差异均无统计学意义(P>0.05)。该模型对于GTV的上下界和CTV2临近的重要器官(如脑干和眼球)的自动勾画有一定的局限性。结论 基于改进后的3D U-Net在多模态影像数据集CT-MRIs上对脑胶质母细胞瘤放疗靶区具有更好的分割效果,显示出较好的临床应用价值。     

Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge

The Cerebral Aneurysm Detection and Analysis (CADA) challenge was organized to support the development and benchmarking of algorithms for detecting, analyzing, and risk assessment of cerebral aneurysms in X-ray rotational angiography (3DRA) images. 109 anonymized 3DRA datasets were provided for training, and 22 additional datasets were used to test the algorithmic solutions. Cerebral aneurysm detection was assessed using the F2 score based on recall and precision, and the fit of the delivered bounding box was assessed using the distance to the aneurysm. The segmentation quality was measured using the Jaccard index and a combination of different surface distance measures. Systematic errors were analyzed using volume correlation and bias. Rupture risk assessment was evaluated using the F2 score. 158 participants from 22 countries registered for the CADA challenge. The U-Net-based detection solutions presented by the community show similar accuracy compared to experts (F2 score 0.92), with a small number of missed aneurysms with diameters smaller than 3.5 mm. In addition, the delineation of these structures, based on U-Net variations, is excellent, with a Jaccard score of 0.92. The rupture risk estimation methods achieved an F2 score of 0.71. The performance of the detection and segmentation solutions is equivalent to that of human experts. The best results are obtained in rupture risk estimation by combining different image-based, morphological, and computational fluid dynamic parameters using machine learning methods. Furthermore, we evaluated the best methods pipeline, from detecting and delineating the vessel dilations to estimating the risk of rupture. The chain of these methods achieves an F2-score of 0.70, which is comparable to applying the risk prediction to the ground-truth delineation (0.71).     

A Novel Task-Based reconstruction approach for digital breast tomosynthesis

The reconstruction of a volumetric image from Digital Breast Tomosynthesis (DBT) measurements is an ill-posed inverse problem, for which existing iterative regularized approaches can provide a good solution. However, the clinical task is somehow omitted in the derivation of those techniques, although it plays a primary role in the radiologist diagnosis. In this work, we address this issue by introducing a novel variational formulation for DBT reconstruction, tailored for a specific clinical task, namely the detection of microcalcifications. Our method aims at simultaneously enhancing the detectability performance and enabling a high-quality restoration of the background breast tissues. Our contribution is threefold. First, we introduce an original task-based reconstruction framework through the proposition of a detectability function inspired from mathematical model observers. Second, we propose a novel total-variation regularizer where the gradient field accounts for the different morphological contents of the imaged breast. Third, we integrate the two developed measures into a cost function, minimized thanks to a new form of the Majorize Minimize Memory Gradient (3MG) algorithm. We conduct a numerical comparison of the convergence speed of the proposed method with those of standard convex optimization algorithms. Experimental results show the interest of our DBT reconstruction approach, qualitatively and quantitatively.     

扇形束CT引导的在线自适应放疗在 宫颈癌中的临床实践

目的 探讨人工智能(AI)辅助联合低剂量扇形束计算机断层扫描(FBCT)引导的在线适应性放疗(OART)治疗宫颈癌的可行性及安全性。方法 在联影uCT-ART平台予11名宫颈癌(10名术后辅助,1名根治性放疗)患者行高年资放疗医师主导触发的OART。分析AI辅助低剂量FBCT引导的OART治疗宫颈癌的可行性,包括评估自动分割轮廓质量、自动放射治疗计划、OART在线剂量学分析、OART流程时长;以及11名宫颈癌患者的放疗相关不良反应分析。结果 在297个分次治疗中,经高年资放疗医师判断,共启动81次OART,人均启动OART 7.4次。OART流程平均总时长为18.97 min OART调整平均时长为15.87 min。11例患者在定位CT上经AI辅助勾画工具自动分割感兴趣区域(ROI),得到ROI_auto经高年资放疗医师修改及审核后得到ROI_edit,其中临床靶区(CTV)的Dice相似系数为0.85 ± 0.04,不劣于前期模型0.89 ± 0.02(P＞0.05),95% 豪斯多夫距离为(5.64 ± 1.60)mm,优于前期模型构建的(6.28 ± 2.31)mm(t=-2.34,P＜0.05)。OART启动后轮廓勾画策略为优先采用CTV刚性拷贝/OAR自动分割+高年资放疗医师修改。OART计划的计划靶区(PTV)剂量分布更为紧凑,且剂量整体更接近处方剂量;OART计划中OAR剂量控制更贴近临床要求,OAR受照剂量显著低于影像引导放疗(IGRT)计划;适形指数CI与均匀性指数HI优于手动计划。在OART过程中,分次间靶区体积变化范围主要集中在±5%的范围内,而OAR体积变化范围波动较大,且无明显规律。消化系统、泌尿系统、造血系统发生急性不良反应均为PRO-CTCAE 1~2级,未见3级及以上的不良反应发生。结论 本研究描述了基于uCT-ART的OART系统在宫颈癌放疗的成功实施,证实了OART临床应用的可行性与安全性。