Nekooeimehr Iman, Lai-Yuen Susana, Bao Paul, Weitzenfeld Alfredo, Hart Stuart
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:2403-2406. doi: 10.1109/EMBC.2016.7591214.
Pelvic organ prolapse is a major health problem in women where pelvic floor organs (bladder, uterus, small bowel, and rectum) fall from their normal position and bulge into the vagina. Dynamic Magnetic Resonance Imaging (DMRI) is presently used to analyze the organs' movements from rest to maximum strain providing complementary support for diagnosis. However, there is currently no automated or quantitative approach to measure the movement of the pelvic organs and their correlation with the severity of prolapse. In this paper, a two-stage method is presented to automatically track and segment pelvic organs on DMRI followed by a multiple-object trajectory classification method to improve the diagnosis of pelvic organ prolapse. Organs are first tracked using particle filters and K-means clustering with prior information. Then, they are segmented using the convex hull of the cluster of particles. Finally, the trajectories of the pelvic organs are modeled using a new Coupled Switched Hidden Markov Model (CSHMM) to classify the severity of pelvic organ prolapse. The tracking and segmentation results are validated using Dice Similarity Index (DSI) whereas the classification results are compared with two manual clinical measurements. Results demonstrate that the presented method is able to automatically track and segment pelvic organs with a DSI above 82% for 26 out of 46 cases and DSI above 75% for all 46 tested cases. The accuracy of the trajectory classification model is also better than current manual measurements.
盆腔器官脱垂是女性面临的一个主要健康问题，即盆底器官（膀胱、子宫、小肠和直肠）从其正常位置掉落并突入阴道。动态磁共振成像（DMRI）目前用于分析器官从静止到最大应变时的运动，为诊断提供辅助支持。然而，目前尚无自动或定量的方法来测量盆腔器官的运动及其与脱垂严重程度的相关性。本文提出了一种两阶段方法，用于在DMRI上自动跟踪和分割盆腔器官，随后采用多目标轨迹分类方法来改善盆腔器官脱垂的诊断。首先使用粒子滤波器和带有先验信息的K均值聚类来跟踪器官。然后，使用粒子簇的凸包对它们进行分割。最后，使用一种新的耦合切换隐马尔可夫模型（CSHMM）对盆腔器官的轨迹进行建模，以对盆腔器官脱垂的严重程度进行分类。使用骰子相似性指数（DSI）对跟踪和分割结果进行验证，而将分类结果与两种手动临床测量结果进行比较。结果表明，所提出的方法能够自动跟踪和分割盆腔器官，46例中有26例的DSI高于82%，所有46例测试病例的DSI均高于75%。轨迹分类模型的准确性也优于当前的手动测量。
