多模态MRI上儿科神经母细胞瘤自动分割：2023年MICCAI SPPIN挑战赛结果
Automated segmentation of pediatric neuroblastoma on multi-modal MRI: Results of the SPPIN challenge at MICCAI 2023
AI总结：
SPPIN挑战赛通过多模态MRI自动分割神经母细胞瘤，展示了预训练网络在小数据集中的有效性，但小肿瘤分割仍需改进。
摘要：
手术在神经母细胞瘤治疗中起着重要作用，这种常见的儿童癌症需要仔细规划，通常通过基于磁共振成像（MRI）的解剖学3D模型进行。然而，创建这些模型通常耗时且依赖用户。我们组织了“小儿神经母细胞瘤手术规划”（SPPIN）挑战赛，以促进该领域的进步，并设立了一个完全自动分割神经母细胞瘤的多模态MRI的基准。挑战分为训练阶段和最终测试阶段。训练阶段中，各队收到了来自34名患者共计78组MRI扫描数据，包括诊断性和化疗后MRI扫描。最终测试阶段包含9名患者的18组MRI数据，决定了各队的排名。排名基于Dice相似性系数（Dice分数）、Hausdorff距离的95百分位数（HD95）和体积相似性（VS）。SPPIN挑战赛于2023年MICCAI举办。最终的排行榜上共有9支队伍。排名第一的队伍在Dice分数上达到0.82，HD95为7.69毫米，VS为0.91，使用了一个大型预训练网络STU-Net。最高排名队伍在诊断性和化疗后MRI扫描之间的分割结果存在显著差异（Dice = 0.89 vs Dice = 0.59，P = 0.01）。SPPIN是首个针对非颅内儿童肿瘤的医学分割挑战赛。最高排名队伍使用了大型预训练网络，表明在小而异质的数据集中预训练可能有帮助。尽管最高排名队伍的结果在大多数患者中表现良好，但对小而已治疗的肿瘤的分割仍不够充分。因此，需要更可靠的分割方法，以创建临床适用的模型，帮助在儿科神经母细胞瘤中进行手术规划。
英文摘要：
Surgery plays an important role within the treatment for neuroblastoma, a common pediatric cancer. This requires careful planning, often via magnetic resonance imaging (MRI)-based anatomical 3D models. However, creating these models is often time-consuming and user dependent. We organized the Surgical Planning in Pediatric Neuroblastoma (SPPIN) challenge, to stimulate developments on this topic, and set a benchmark for fully automatic segmentation of neuroblastoma on multi-model MRI. The challenge started with a training phase, where teams received 78 sets of MRI scans from 34 patients, consisting of both diagnostic and post-chemotherapy MRI scans. The final test phase, consisting of 18 MRI sets from 9 patients, determined the ranking of the teams. Ranking was based on the Dice similarity coefficient (Dice score), the 95th percentile of the Hausdorff distance (HD95) and the volumetric similarity (VS). The SPPIN challenge was hosted at MICCAI 2023. The final leaderboard consisted of 9 teams. The highest-ranking team achieved a median Dice score 0.82, a median HD95 of 7.69 mm and a VS of 0.91, utilizing a large, pretrained network called STU-Net. A significant difference for the segmentation results between diagnostic and post-chemotherapy MRI scans was observed (Dice = 0.89 vs Dice = 0.59, P = 0.01) for the highest-ranking team. SPPIN is the first medical segmentation challenge in extracranial pediatric oncology. The highest-ranking team used a large pre-trained network, suggesting that pretraining can be of use in small, heterogenous datasets. Although the results of the highest-ranking team were high for most patients, segmentation especially in small, pre-treated tumors were insufficient. Therefore, more reliable segmentation methods are needed to create clinically applicable models to aid surgical planning in pediatric neuroblastoma.
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