论文标题
通过脊柱整流和解剖约束优化在CT中的自动椎骨定位和识别
Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization
论文作者
论文摘要
在脊柱疾病诊断和手术计划的许多临床应用中，需要准确的椎骨定位和鉴定。然而，这项任务在高度变化的病理（例如椎骨压缩裂缝，脊柱侧弯和椎骨固定）和成像条件（例如有限的视野和金属条纹伪像）中提出了重大挑战。本文提出了一种强大而准确的方法，可以有效利用脊柱的解剖学知识，以促进椎骨定位和识别。训练了一个关键点定位模型，以产生椎骨中心的激活图。然后将它们沿着脊柱中心线重新采样，以产生脊柱切除的激活图，该图被进一步聚集成1D激活信号。此后，引入了一个解剖约束的优化模块，以在软限制下共同搜索最佳椎骨中心，该中心调节椎骨之间的距离和对连续椎骨指数的硬约束。当通过302个高度病理CT图像的主要公共基准进行评估时，该方法报告了最佳识别状态（ID。）97.4％，并且胜过94.7％ID的最佳竞争方法。通过减少相对ID来率。错误率提高一半。
Accurate vertebra localization and identification are required in many clinical applications of spine disorder diagnosis and surgery planning. However, significant challenges are posed in this task by highly varying pathologies (such as vertebral compression fracture, scoliosis, and vertebral fixation) and imaging conditions (such as limited field of view and metal streak artifacts). This paper proposes a robust and accurate method that effectively exploits the anatomical knowledge of the spine to facilitate vertebra localization and identification. A key point localization model is trained to produce activation maps of vertebra centers. They are then re-sampled along the spine centerline to produce spine-rectified activation maps, which are further aggregated into 1-D activation signals. Following this, an anatomically-constrained optimization module is introduced to jointly search for the optimal vertebra centers under a soft constraint that regulates the distance between vertebrae and a hard constraint on the consecutive vertebra indices. When being evaluated on a major public benchmark of 302 highly pathological CT images, the proposed method reports the state of the art identification (id.) rate of 97.4%, and outperforms the best competing method of 94.7% id. rate by reducing the relative id. error rate by half.
