首个可解释的产时超声胎儿头位评估模型，精准测量分娩关键指标。
Sequential Spatial-Temporal Network for Interpretable Automatic Ultrasonic Assessment of Fetal Head during labor
- 分步处理：先定位超声切面，再分割解剖结构，最后检测关键点
- 相比现有模型，AoP误差降18%，HSD误差降22%
- 专为临床流程设计，结果可解释，适合产科医生辅助决策
ISUOG产时超声指南强调角进展（AoP）和头耻距（HSD）是评估胎儿头下降和预测分娩结局的关键指标。准确测量需按标准化流程：识别标准超声切面，检测与分娩参数相关的耻骨联合及胎儿头部位解剖标志，再基于标志点计算测量值。针对此临床需求与操作流程，本文提出首个专用于产时超声视频分析的可解释模型——序列时空网络（SSTN）。SSTN通过依次识别超声切面、分割耻骨联合与胎儿头等解剖结构，并检测关键标志点，实现对HSD和AoP的精确测量。该框架融合任务相关信息，提升精度与可靠性。在临床数据集上的实验表明，SSTN显著优于现有模型，AoP平均绝对误差降低18%，HSD降低22%。
原文摘要 · Abstract (English)
The intrapartum ultrasound guideline established by ISUOG highlights the Angle of Progression (AoP) and Head Symphysis Distance (HSD) as pivotal metrics for assessing fetal head descent and predicting delivery outcomes. Accurate measurement of the AoP and HSD requires a structured process. This begins with identifying standardized ultrasound planes, followed by the detection of specific anatomical landmarks within the regions of the pubic symphysis and fetal head that correlate with the delivery parameters AoP and HSD. Finally, these measurements are derived based on the identified anatomical landmarks. Addressing the clinical demands and standard operation process outlined in the ISUOG guideline, we introduce the Sequential Spatial-Temporal Network (SSTN), the first interpretable model specifically designed for the video of intrapartum ultrasound analysis. The SSTN operates by first identifying ultrasound planes, then segmenting anatomical structures such as the pubic symphysis and fetal head, and finally detecting key landmarks for precise measurement of HSD and AoP. Furthermore, the cohesive framework leverages task-related information to improve accuracy and reliability. Experimental evaluations on clinical datasets demonstrate that SSTN significantly surpasses existing models, reducing the mean absolute error by 18% for AoP and 22% for HSD.
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