基于OpenVINO的三维头颅测量标志回归任务的dl推理

E. Vasiliev, D. Lachinov, A. Getmanskaya
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摘要

在本文中,我们评估了英特尔发行的OpenVINO工具包在实际解决使用深度学习方法的自动三维头测量分析问题中的性能。今年,作者提出了一种从ct断层扫描数据中检测头侧标志的方法,该方法可以抵抗颅骨畸形,并使用卷积神经网络(CNN)。对变形的抵抗是由于对头骨形状参数化基本的4个点的初始检测。CNN在三个架构上探索了这种方法。与类似物相比,获得了创纪录的回归精度。本文对训练好的cnn模型在推理阶段的决策性能进行了评价。为了进行比较研究,我们选择了OpenVINO的PyTorch和Intel Distribution计算环境,以及3种CNN架构中的2种:基于VGG的头颅测量地标回归和基于hourglass的模型,使用RexNext主干进行地标热图回归。实验数据集由20例获得性颅颌面畸形患者的CT组成,包括术前和术后CT扫描,格式为800x800x496,体素间距为0.2x0.2x0.2 mm。使用OpenVINO的性能比PyTorch有了很大的提高,对于直接回归模型,推理速度从13倍提高到16倍,对于更复杂和精确的沙漏模型,推理速度从3.5倍提高到3.8倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DL-inferencing for 3D Cephalometric Landmarks Regression task using OpenVINO
In this paper, we evaluate the performance of the Intel Distribution of OpenVINO toolkit in practical solving of the problem of automatic three-dimensional Cephalometric analysis using deep learning methods. This year, the authors proposed an approach to the detection of cephalometric landmarks from CT-tomography data, which is resistant to skull deformities and use convolutional neural networks (CNN). Resistance to deformations is due to the initial detection of 4 points that are basic for the parameterization of the skull shape. The approach was explored on CNN for three architectures. A record regression accuracy in comparison with analogs was obtained. This paper evaluates the perfor- mance of decision making for the trained CNN-models at the inference stage. For a comparative study, the computing environments PyTorch and Intel Distribution of OpenVINO were selected, and 2 of 3 CNN architectures: based on VGG for regression of cephalometric landmarks and an Hourglass-based model, with the RexNext backbone for the land- marks heatmap regression. The experimental dataset was consist of 20 CT of patients with acquired craniomaxillofacial deformities and was in- clude pre- and post-operative CT scans whose format is 800x800x496 with voxel spacing of 0.2x0.2x0.2 mm. Using OpenVINO showed a great increase in performance over the PyTorch, with inference speedup from 13 to 16 times for a Direct Regression model and from 3.5 to 3.8 times for a more complex and precise Hourglass model.
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