Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency.
Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency.
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DOI：
10.1038/s41598-022-11785-6
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发表时间：
2022-05-12
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
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作者：
将医学图像分割成不同的组织类型是骨科研究取得许多进展的必要条件；然而，手动分割技术可能是时间和成本过高。这项工作的目的是开发一种半自动分割算法，该算法利用空间强度梯度将髌骨从不需要训练集的膝关节磁共振（MR）图像中分离出来。利用磁共振成像（MRI）和计算机断层扫描（CT）在四名人类参与者（体内）和三个猪膝关节（离体）的样本中验证了所开发的算法。我们评估了半自动分割技术在以下情况下的重复性（以均数±标准差表示）：(1)同一次MRI扫描两次(Dice相似系数= 0.988±0.002；(2)分割技术的扫描/再扫描重复性（表面距离= - 0.02±0.03 mm），(3)半自动分割技术与手动MRI分割技术（表面距离= - 0.02±0.08 mm）的比较，以及(4)半自动分割技术在应用于同一标本的MRI和CT图像时的比较（表面距离= - 0.02±0.06 mm）。计算髌骨模型对之间垂直于软骨表面的平均表面距离。关键是，在这项工作中开发的半自动分割算法减少了大约75%的分割时间。该方法有望提高研究吞吐量，并有可能用于生成深度学习算法的训练数据。
Segmentation of medical images into different tissue types is essential for many advancements in orthopaedic research; however, manual segmentation techniques can be time- and cost-prohibitive. The purpose of this work was to develop a semi-automatic segmentation algorithm that leverages gradients in spatial intensity to isolate the patella bone from magnetic resonance (MR) images of the knee that does not require a training set. The developed algorithm was validated in a sample of four human participants (in vivo) and three porcine stifle joints (ex vivo) using both magnetic resonance imaging (MRI) and computed tomography (CT). We assessed the repeatability (expressed as mean ± standard deviation) of the semi-automatic segmentation technique on: (1) the same MRI scan twice (Dice similarity coefficient = 0.988 ± 0.002; surface distance = − 0.01 ± 0.001 mm), (2) the scan/re-scan repeatability of the segmentation technique (surface distance = − 0.02 ± 0.03 mm), (3) how the semi-automatic segmentation technique compared to manual MRI segmentation (surface distance = − 0.02 ± 0.08 mm), and (4) how the semi-automatic segmentation technique compared when applied to both MRI and CT images of the same specimens (surface distance = − 0.02 ± 0.06 mm). Mean surface distances perpendicular to the cartilage surface were computed between pairs of patellar bone models. Critically, the semi-automatic segmentation algorithm developed in this work reduced segmentation time by approximately 75%. This method is promising for improving research throughput and potentially for use in generating training data for deep learning algorithms.
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DOI：
10.1148/rg.2017160130
发表时间：
2017-03
期刊：
Radiographics : a review publication of the Radiological Society of North America, Inc
影响因子：
--
作者：
Erickson BJ;Korfiatis P;Akkus Z;Kline TL
通讯作者：
Kline TL
影响因子：
2.4
作者：
Englander ZA;Martin JT;Ganapathy PK;Garrett WE;DeFrate LE
通讯作者：
DeFrate LE
影响因子：
9.3
作者：
Delso, Gaspar;Wiesinger, Florian;Veit-Haibach, Patrick
通讯作者：
Veit-Haibach, Patrick
影响因子：
9
作者：
CHANG, AE;MATORY, YL;ROSENBERG, SA
通讯作者：
ROSENBERG, SA
影响因子：
2.4
作者：
Owusu-Akyaw, Kwadwo A.;Heckelman, Lauren N.;DeFrate, Louis E.
通讯作者：
DeFrate, Louis E.