Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, Toronto, ON, Canada.
Calavera Surgical Design Inc., Toronto, ON, Canada.
Int J Comput Assist Radiol Surg. 2024 Apr;19(4):747-756. doi: 10.1007/s11548-024-03068-4. Epub 2024 Mar 2.
New deep learning and statistical shape modelling approaches aim to automate the design process for patient-specific cranial implants, as highlighted by the MICCAI AutoImplant Challenges. To ensure applicability, it is important to determine if the training data used in developing these algorithms represent the geometry of implants designed for clinical use.
Calavera Surgical Design provided a dataset of 206 post-craniectomy skull geometries and their clinically used implants. The MUG500+ dataset includes 29 post-craniectomy skull geometries and implants designed for automating design. For both implant and skull shapes, the inner and outer cortical surfaces were segmented, and the thickness between them was measured. For the implants, a 'rim' was defined that transitions from the repaired defect to the surrounding skull. For unilateral defect cases, skull implants were mirrored to the contra-lateral side and thickness differences were quantified.
The average thickness of the clinically used implants was 6.0 ± 0.5 mm, which approximates the thickness on the contra-lateral side of the skull (relative difference of -0.3 ± 1.4 mm). The average thickness of the MUG500+ implants was 2.9 ± 1.0 mm, significantly thinner than the intact skull thickness (relative difference of 2.9 ± 1.2 mm). Rim transitions in the clinical implants (average width of 8.3 ± 3.4 mm) were used to cap and create a smooth boundary with the skull.
For implant modelers or manufacturers, this shape analysis quantified differences of cranial implants (thickness, rim width, surface area, and volume) to help guide future automated design algorithms. After skull completion, a thicker implant can be more versatile for cases involving muscle hollowing or thin skulls, and wider rims can smooth over the defect margins to provide more stability. For clinicians, the differing measurements and implant designs can help inform the options available for their patient specific treatment.
新的深度学习和统计形状建模方法旨在通过 MICCAI AutoImplant 挑战赛实现针对患者特定颅植入物的设计过程自动化。为了确保适用性，重要的是要确定开发这些算法中使用的训练数据是否代表临床使用的植入物的几何形状。
Calavera Surgical Design 提供了 206 例去骨瓣术后颅骨几何形状及其临床使用的植入物数据集。MUG500+数据集包括 29 例去骨瓣术后颅骨几何形状和用于自动化设计的植入物。对于植入物和颅骨形状，都对内外皮质表面进行了分割，并测量了它们之间的厚度。对于植入物，定义了一个“边缘”，从修复缺陷过渡到周围颅骨。对于单侧缺陷病例，将颅骨植入物镜像到对侧，并量化厚度差异。
临床使用的植入物的平均厚度为 6.0±0.5mm，接近颅骨对侧的厚度（相对差异为-0.3±1.4mm）。MUG500+植入物的平均厚度为 2.9±1.0mm，明显比完整颅骨厚度薄（相对差异为 2.9±1.2mm）。临床植入物中的边缘过渡（平均宽度为 8.3±3.4mm）用于覆盖颅骨并形成与颅骨的平滑边界。
对于植入物建模者或制造商来说，这种形状分析量化了颅骨植入物（厚度、边缘宽度、表面积和体积）的差异，有助于指导未来的自动设计算法。颅骨完成后，较厚的植入物对于涉及肌肉凹陷或颅骨较薄的病例更为通用，较宽的边缘可以平滑覆盖缺陷边缘，提供更稳定的效果。对于临床医生来说，不同的测量和植入物设计可以帮助他们了解针对特定患者的治疗方案的选择。
