根据计算机断层扫描(CT)图像进行半自动膝关节骨骼分割和重建的方法框架。

IF 2.9 2区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Quantitative Imaging in Medicine and Surgery Pub Date : 2024-10-01 Epub Date: 2024-09-26 DOI:10.21037/qims-24-821
Ahsan Humayun, Mustafain Rehman, Bin Liu
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引用次数: 0

摘要

背景:准确划分膝关节骨边界对于膝关节疾病的计算机辅助诊断(CAD)和有效治疗计划至关重要。由于膝关节的复杂性,包括错综复杂的骨结构和重叠的软组织,目前的方法往往难以精确分割。患者解剖结构和图像质量的变化使这些挑战变得更加复杂,这凸显了对改进技术的需求。本文提出了一种从连续计算机断层扫描(CT)图像中提取膝关节骨骼的新型半自动分割方法:我们的方法将模糊 C-means (FCM) 算法与基于区域的自适应主动轮廓模型 (ACM) 相结合。最初,FCM 算法为每个体素分配成员度,根据它们属于特定骨骼区域的可能性,将骨骼区域与周围软组织区分开来。随后,基于区域的自适应 ACM 利用这些成员度来指导轮廓演化和细化分割边界。为确保临床适用性,我们使用行进立方体算法进一步增强了我们的方法,以重建三维(3D)模型。我们在随机选取的六个膝关节上对该方法进行了评估:我们使用 Dice 系数、灵敏度、特异性和几何评估等定量指标对该方法进行了评估。我们的方法在股骨(98.95%)、胫骨(98.10%)和髌骨(97.14%)上都获得了较高的 Dice 分数,显示了卓越的准确性。胫骨、股骨(0.5±0.14 毫米)和髌骨(0.6±0.13 毫米)的均方根距离(RSD)值明显较低,表明分割精确:结论:所提出的方法为膝关节病变的 CAD 系统提供了重大进展。我们的方法在实现膝关节骨骼精确分割方面表现出卓越的性能,为解剖分析、手术规划和患者特制假体提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A method framework of semi-automatic knee bone segmentation and reconstruction from computed tomography (CT) images.

Background: Accurate delineation of knee bone boundaries is crucial for computer-aided diagnosis (CAD) and effective treatment planning in knee diseases. Current methods often struggle with precise segmentation due to the knee joint's complexity, which includes intricate bone structures and overlapping soft tissues. These challenges are further complicated by variations in patient anatomy and image quality, highlighting the need for improved techniques. This paper presents a novel semi-automatic segmentation method for extracting knee bones from sequential computed tomography (CT) images.

Methods: Our approach integrates the fuzzy C-means (FCM) algorithm with an adaptive region-based active contour model (ACM). Initially, the FCM algorithm assigns membership degrees to each voxel, distinguishing bone regions from surrounding soft tissues based on their likelihood of belonging to specific bone regions. Subsequently, the adaptive region-based ACM utilizes these membership degrees to guide the contour evolution and refine segmentation boundaries. To ensure clinical applicability, we further enhance our method using the marching cubes algorithm to reconstruct a three-dimensional (3D) model. We evaluated the method on six randomly selected knee joints.

Results: We evaluated the method using quantitative metrics such as the Dice coefficient, sensitivity, specificity, and geometrical assessment. Our method achieved high Dice scores for the femur (98.95%), tibia (98.10%), and patella (97.14%), demonstrating superior accuracy. Remarkably low root mean square distance (RSD) values were obtained for the tibia and femur (0.5±0.14 mm) and patella (0.6±0.13 mm), indicating precise segmentation.

Conclusions: The proposed method offers significant advancements in CAD systems for knee pathologies. Our approach demonstrates superior performance in achieving precise and accurate segmentation of knee bones, providing valuable insights for anatomical analysis, surgical planning, and patient-specific prostheses.

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来源期刊
Quantitative Imaging in Medicine and Surgery
Quantitative Imaging in Medicine and Surgery Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
4.20
自引率
17.90%
发文量
252
期刊介绍: Information not localized
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