Brignol Arnaud, Cheriet Farida, Aubin-Fournier Jean-François, Fortin Carole, Laporte Catherine
Department of Electrical Engineering, École de technologie supérieure, 1100, Rue Notre-Dame Ouest, Montreal, QC, H3C 1K3, Canada.
Department of Computer Engineering and Software Engineering, Polytechnique Montréal, 2900, boul. Édouard-Montpetit, Montreal, QC, H3T 1J4, Canada.
Int J Comput Assist Radiol Surg. 2025 Jan;20(1):97-106. doi: 10.1007/s11548-024-03249-1. Epub 2024 Sep 17.
Ultrasound imaging has emerged as a promising cost-effective and portable non-irradiant modality for the diagnosis and follow-up of diseases. Motion analysis can be performed by segmenting anatomical structures of interest before tracking them over time. However, doing so in a robust way is challenging as ultrasound images often display a low contrast and blurry boundaries.
In this paper, a robust descriptor inspired from the fractal dimension is presented to locally characterize the gray-level variations of an image. This descriptor is an adaptive grid pattern whose scale locally varies as the gray-level variations of the image. Robust features are then located based on the gray-level variations, which are more likely to be consistently tracked over time despite the presence of noise.
The method was validated on three datasets: segmentation of the left ventricle on simulated echocardiography (Dice coefficient, DC), accuracy of diaphragm motion tracking for healthy subjects (mean sum of distances, MSD) and for a scoliosis patient (root mean square error, RMSE). Results show that the method segments the left ventricle accurately ( ) and robustly tracks the diaphragm motion for healthy subjects ( mm) and for the scoliosis patient ( mm).
This method has the potential to segment structures of interest according to their texture in an unsupervised fashion, as well as to help analyze the deformation of tissues. Possible applications are not limited to US image. The same principle could also be applied to other medical imaging modalities such as MRI or CT scans.
超声成像已成为一种有前景的、具有成本效益且便携的非辐射性疾病诊断及随访方式。运动分析可通过在随时间跟踪感兴趣的解剖结构之前对其进行分割来执行。然而，由于超声图像通常对比度低且边界模糊，以稳健的方式做到这一点具有挑战性。
本文提出一种受分形维启发的稳健描述符，用于局部表征图像的灰度变化。该描述符是一种自适应网格模式，其尺度会随着图像的灰度变化而局部变化。然后基于灰度变化定位稳健特征，这些特征在存在噪声的情况下更有可能随时间被一致地跟踪。
该方法在三个数据集上得到验证：模拟超声心动图上左心室的分割（骰子系数，DC）、健康受试者膈肌运动跟踪的准确性（平均距离总和，MSD）以及一名脊柱侧弯患者的膈肌运动跟踪准确性（均方根误差，RMSE）。结果表明，该方法能准确分割左心室（ ），并能稳健地跟踪健康受试者（ 毫米）和脊柱侧弯患者（ 毫米）的膈肌运动。
该方法有潜力以无监督的方式根据感兴趣结构的纹理对其进行分割，并有助于分析组织的变形。可能的应用不限于超声图像。相同的原理也可应用于其他医学成像模态，如MRI或CT扫描。
