Anatomical landmark detection on bi-planar radiographs for predicting spinopelvic parameters.

IF 1.6 Q3 CLINICAL NEUROLOGY Spine deformity Pub Date : 2024-10-23 DOI:10.1007/s43390-024-00990-0
Stefan Lang, Moritz Jokeit, Ji Hyun Kim, Lukas Urbanschitz, Luca Fisler, Carlos Torrez, Frédéric Cornaz, Jess G Snedeker, Mazda Farshad, Jonas Widmer
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Abstract

Introduction: Accurate landmark detection is essential for precise analysis of anatomical structures, supporting diagnosis, treatment planning, and monitoring in patients with spinal deformities. Conventional methods rely on laborious landmark identification by medical experts, which motivates automation. The proposed deep learning pipeline processes bi-planar radiographs to determine spinopelvic parameters and Cobb angles without manual supervision.

Methods: The dataset used for training and evaluation consisted of 555 bi-planar radiographs from un-instrumented patients, which were manually annotated by medical professionals. The pipeline performed a pre-processing step to determine regions of interest, including the cervical spine, thoracolumbar spine, sacrum, and pelvis. For each ROI, a segmentation network was trained to identify vertebral bodies and pelvic landmarks. The U-Net architecture was trained on 455 bi-planar radiographs using binary cross-entropy loss. The post-processing algorithm determined spinal alignment and angular parameters based on the segmentation output. We evaluated the pipeline on a test set of 100 previously unseen bi-planar radiographs, using the mean absolute difference between annotated and predicted landmarks as the performance metric. The spinopelvic parameter predictions of the pipeline were compared to the measurements of two experienced medical professionals using intraclass correlation coefficient (ICC) and mean absolute deviation (MAD).

Results: The pipeline was able to successfully predict the Cobb angles in 61% of all test cases and achieved mean absolute differences of 3.3° (3.6°) and averaged ICC of 0.88. For thoracic kyphosis, lumbar lordosis, sagittal vertical axis, sacral slope, pelvic tilt, and pelvic incidence, the pipeline produced reasonable outputs in 69%, 58%, 86%, 85%, 84%, and 84% of the cases. The MAD was 5.6° (7.8°), 4.7° (4.3°), 2.8 mm (3.0 mm), 4.5° (7.2°), 1.8° (1.8°), and 5.3° (7.7°), while the ICC was measured at 0.69, 0.82, 0.99, 0.61, 0.96, and 0.70, respectively.

Conclusion: Despite limitations in patients with severe pathologies and high BMI, the pipeline automatically predicted coronal and sagittal spinopelvic parameters, which has the potential to simplify clinical routines and large-scale retrospective data analysis.

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在双平面X光片上检测解剖地标,以预测脊柱骨盆参数。
简介准确的地标检测对于精确分析解剖结构、支持脊柱畸形患者的诊断、治疗计划和监测至关重要。传统方法依赖于医学专家费力的地标识别,因此需要实现自动化。所提出的深度学习管道可处理双平面射线照片,在无需人工监督的情况下确定脊柱骨盆参数和 Cobb 角:用于训练和评估的数据集由 555 张来自未接受仪器检查的患者的双平面放射照片组成,这些照片由医疗专业人员手动标注。管道执行预处理步骤以确定感兴趣区域,包括颈椎、胸腰椎、骶骨和骨盆。对于每个感兴趣区,都会训练一个分割网络来识别椎体和骨盆地标。使用二元交叉熵损失对 455 张双平面射线照片进行了 U-Net 架构训练。后处理算法根据分割输出确定脊柱排列和角度参数。我们使用注释地标和预测地标之间的平均绝对差值作为性能指标,在由 100 张之前未见过的双平面射线照片组成的测试集上对该管道进行了评估。使用类内相关系数(ICC)和平均绝对偏差(MAD)将管道的脊柱骨盆参数预测结果与两位经验丰富的医学专家的测量结果进行比较:结果:在所有测试案例中,管道能够成功预测 61% 的 Cobb 角,平均绝对偏差为 3.3° (3.6°),平均 ICC 为 0.88。对于胸椎后凸、腰椎前凸、矢状垂直轴、骶骨斜度、骨盆倾斜和骨盆内陷,该管道分别在 69%、58%、86%、85%、84% 和 84% 的病例中产生了合理的输出结果。MAD分别为5.6°(7.8°)、4.7°(4.3°)、2.8 mm(3.0 mm)、4.5°(7.2°)、1.8°(1.8°)和5.3°(7.7°),ICC分别为0.69、0.82、0.99、0.61、0.96和0.70:尽管在严重病变和高体重指数患者中存在局限性,但该管道能自动预测冠状面和矢状面的脊柱骨盆参数,具有简化临床常规和大规模回顾性数据分析的潜力。
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来源期刊
CiteScore
3.20
自引率
18.80%
发文量
167
期刊介绍: Spine Deformity the official journal of the?Scoliosis Research Society is a peer-refereed publication to disseminate knowledge on basic science and clinical research into the?etiology?biomechanics?treatment?methods and outcomes of all types of?spinal deformities. The international members of the Editorial Board provide a worldwide perspective for the journal's area of interest.The?journal?will enhance the mission of the Society which is to foster the optimal care of all patients with?spine?deformities worldwide. Articles published in?Spine Deformity?are Medline indexed in PubMed.? The journal publishes original articles in the form of clinical and basic research. Spine Deformity will only publish studies that have institutional review board (IRB) or similar ethics committee approval for human and animal studies and have strictly observed these guidelines. The minimum follow-up period for follow-up clinical studies is 24 months.
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