In this paper, we address the problem of automatic three-dimensional cephalometric analysis. Cephalometric analysis performed on lateral radiographs doesn’t fully exploit the structure of 3D objects due to projection onto the lateral plane. With the development of three-dimensional imaging techniques such as CT, several analysis methods have been proposed that extend to the 3D case. The analysis based on these methods is invariant to rotations and translations and can describe difficult skull deformation, where 2D cephalometry has no use. In this paper, we provide a wide overview of existing approaches for cephalometric landmark regression. Moreover, we perform a series of experiments with state of the art 3D convolutional neural network (CNN) based methods for keypoint regression: direct regression with CNN, heatmap regression and Softargmax regression. For the first time, we extensively evaluate the described methods and demonstrate their effectiveness in the estimation of Frankfort Horizontal and cephalometric points locations for patients with severe skull deformations. We demonstrate that Heatmap and Softargmax regression models provide sufficient regression error for medical applications (less than 4 mm). Moreover, the Softargmax model achieves 1.15° inclination error for the Frankfort horizontal. For the fair comparison with the prior art, we also report results projected on the lateral plane.

Personalizing the three-dimensional atlas model (template) of an organ under analysis based on the data of patient’s three-dimensional examination is an important task for modern digital...

A technology for the automatic multi-class labeling of brain electron microscopy (EM) objects needed to create large synthetic datasets, which could be used for brain cell segmentation tasks, is proposed. The main research tools were a generative diffusion AI model and a U-Net-like segmentation model. The technology was studied on the segmentation task of up to six brain organelles. The initial dataset used was the popular EPFL dataset labeled for the mitochondria class, which has training and test parts having 165 layers each. Our mark up for the EPFL dataset was named EPFL6 and contained six classes. The technology was implemented and studied in a two-step experiment: (1) dataset synthesis using a diffusion model trained on EPFL6; (2) evaluation of the labeling accuracy of a multi-class synthetic dataset by the segmentation accuracy on the test part of EPFL6. It was found that (1) the segmentation accuracy of the mitochondria class for the diffusion synthetic datasets corresponded to the accuracy of the original ones; (2) augmentation via geometric synthetics provided a better accuracy for underrepresented classes; (3) the naturalization of geometric synthetics by the diffusion model yielded a positive effect; (4) due to the augmentation of the 165 layers of the original EPFL dataset with diffusion synthetics, it was possible to achieve and surpass the record accuracy of Dice = 0.948, which was achieved using 3D estimation in Hive-net (2021).

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.