The high risk of maternal and perinatal morbidity is associated with longer labor duration due to the slow progression of fetal descent, but accurate assessment of fetal descent by monitoring the fetal head (FH) station remains a clinical challenge in guiding obstetric management. Based on clinical findings, the transvaginal digital examination is the most commonly used clinical estimation method of fetal station. However, this traditional approach is very subjective, often difficult, and unreliable. The need of an objective diagnosis found its solution in the use of transperineal ultrasound (TPU) able to assess FH station by measuring the angle of progression (AoP) that is the extension the FH goes through in its descent. Manual segmentation of symphysis pubis (SP)-fetal head from ITU images for clinical radiologists is considered as the most reliable but extremely time-consuming procedure prone to subjectivity and large inter-observer variability. With the rapid development of artificial intelligence in medical images, automatic measurement algorithms based on ITU images are expected to solve the above problems. This segmentation competition requires the use of MHA files for image data, which includes 4000 samples of both original images and ground truth labels. The original images have a shape format of 3x256x256. The ground truth labels have a shape format of 256x256, and contain pixels labeled as 0, 1, or 2, where 0 represents the background, 1 represents the pubic symphysis, and 2 represents the fetal head. For the competition evaluation, only prediction images with a size of 256x256 are accepted. The prediction images can only contain pixels labeled as 0, 1, or 2, with 0 representing the background, 1 representing the pubic symphysis, and 2 representing the fetal head. PSFHS is the test dataset. Each needs to send a email including the name, and affiliations to the organizer via email: bai_jieyun@126.com