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首页> 外文会议>International conference on medical imaging computing and computer-assisted intervention >Automatic Craniomaxillofacial Landmark Digitization via Segmentation-Guided Partially-Joint Regression Forest Model
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Automatic Craniomaxillofacial Landmark Digitization via Segmentation-Guided Partially-Joint Regression Forest Model

机译：通过分段引导的部分联合回归森林模型自动进行颅颌面地标数字化

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摘要

Craniomaxillofacial (CMF) deformities involve congenital and acquired deformities of the head and face. Landmark digitization is a critical step in quantifying CMF deformities. In current clinical practice, CMF landmarks have to be manually digitized on 3D models, which is time-consuming. To date, there is no clinically acceptable method that allows automatic landmark digitization, due to morphological variations among different patients and artifacts of cone-beam computed tomography (CBCT) images. To address these challenges, we propose a segmentation-guided partially-joint regression forest model that can automatically digitizes CMF landmarks. In this model, a regression voting strategy is first adopted to localize landmarks by aggregating evidences from context locations, thus potentially relieving the problem caused by image artifacts near the landmark. Second, segmentation is also utilized to resolve inconsistent landmark appearances that are caused by morphological variations among different patients, especially on the teeth. Third, a partially-joint model is proposed to separately localize landmarks based on coherence of landmark positions to improve digitization reliability. The experimental results show that the accuracy of automatically digitized landmarks using our approach is clinically acceptable.
机译：颅颌面部（CMF）畸形涉及先天性和后天性的头部和面部畸形。具有里程碑意义的数字化是量化CMF畸形的关键步骤。在当前的临床实践中，必须在3D模型上手动数字化CMF界标，这非常耗时。迄今为止，由于不同患者之间的形态变化和锥束计算机断层扫描（CBCT）图像的伪影，目前尚无一种临床上可接受的方法可以自动对地标进行数字化。为了解决这些挑战，我们提出了一种以细分为指导的部分联合回归森林模型，该模型可以自动将CMF界标数字化。在此模型中，首先采用回归投票策略，通过从上下文位置聚集证据来对地标进行定位，从而潜在地缓解了由地标附近的图像伪影引起的问题。其次，分割还用于解决由不同患者（尤其是牙齿）之间的形态变化引起的不一致的界标外观。第三，提出了一种部分联合模型，基于地标位置的相干性来分别定位地标，以提高数字化的可靠性。实验结果表明，使用我们的方法自动数字化地标的准确性在临床上是可以接受的。

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