O-CCR: oriented cervical canal region detection framework toward cervical change assessment in transvaginal...

The cervix undergoes morphological and structural changes during pregnancy in preparation for delivery. Assessing the progression of these changes using transvaginal ultrasound (TVUS) is crucial for preterm birth prediction. However, existing methods such as cervical length have limitations in capturing subtle tissue changes. Although tissue analysis using TVUS has been explored to address these limitations, achieving consistent and reproducible results in quantitative analysis remains challenging due to high inter-observer variability and a lack of standardized region of interest (ROI) definitions. This study proposes an oriented cervical canal region (O-CCR) framework that identifies regions containing ultrasound features while preserving anatomical spatial information.

We utilized 1436 TVUS images for training, validation, and testing, with 189 additional images from a different hospital for external validation. CCR was defined to include the cervical canal and its surrounding region after aligning the IO and EO parallel to ensure anatomical consistency in the cervix. To validate the effectiveness of O-CCR in handling various orientations, we applied five oriented object detection models (Oriented R-CNN, ReDet, S²A-Net, R³Det, and Oriented RepPoints) and evaluated their CCR localization performance.

We compared the performance of five models implemented within O-CCR framework. Among them, Oriented RepPoints achieved the highest average precision (AP) of 0.981 at the intersection over union (IoU) threshold of 0.5, compared to Oriented R-CNN (0.968), S²A-Net (0.962), ReDet (0.964), and R³Det (0.980) on the test dataset. Notably, Oriented RepPoints demonstrated superior performance even at higher thresholds of 0.6 (0.931) and 0.7 (0.743) and the lowest average orientation error (AOE) of 9.1980 in CCR localization.

The proposed O-CCR framework exhibited reliable performance in CCR localization regardless of varying orientations and morphological configurations, while providing standardized regions that preserve the anatomical spatial information of the cervix. The consistent CCR could be applied to quantitative analysis of cervical tissue properties in future research. Ultimately, this approach could support the development of automated cervical change assessment for prenatal care.