Two-stream Graph Convolutional Networks with Task-specific Loss for Dual-task Gait Analysis.

Dual-task gait systems can be utilized to assess elderly patients for cognitive decline. Although numerous research studies have been conducted to estimate cognitive scores, this field still faces two significant challenges. Firstly, it is crucial to fully utilize dual-task cost representations for diagnosis. Secondly, the design of optimal strategies for effectively extracting dual-task cost representations remains a challenge. To address these issues, in this paper, we propose a deep learning-based framework that implements a spatio-temporal graph convolutional neural network (ST-GCN) with single-task and dual-task pathways for cognitive impairment detection in gait. We also introduce a novel loss, termed task-specific loss, to ensure that single-task and dual-task representations are distinguishable from each other. Furthermore, dual-task cost representations are calculated as the difference between dual-task and single-task representations, which are resilient to individual differences and contribute to the robustness of the framework. These representations provide a comprehensive view of single-task and dual-task gait information to generate task predictions. The proposed framework outperforms existing approaches with a sensitivity of 0.969 and a specificity of 0.940 for cognitive impairment detection.

Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase.

Large slice thickness or slice increment causes information insufficiency of Computed Tomography (CT) data in the longitudinal direction, which degrades the quality of CT-based diagnosis. Traditional approaches such as high-resolution computed tomography (HRCT) and linear interpolation can solve this problem. However, HRCT suffers from dose increase, and linear interpolation causes artifacts. In this study, we propose a deep-learning-based approach to reconstruct densely sliced CT from sparsely sliced CT data without any dose increase. The proposed method reconstructs CT images from neighboring slices using a U-net architecture. To prevent multiple reconstructed slices from influencing one another, we propose a parallel architecture in which multiple U-net architectures work independently. Moreover, for a specific organ (i.e., the liver), we propose a range-clip technique to improve reconstruction quality, which enhances the learning of CT values within this organ by enlarging the range of the training data. CT data from 130 patients were collected, with 80% used for training and the remaining 20% used for testing. Experiments showed that our parallel U-net architecture reduced the mean absolute error of CT values in the reconstructed slices by 22.05%, and also reduced the incidence of artifacts around the boundaries of target organs, compared with linear interpolation. Further improvements of 15.12%, 11.04%, 10.94%, and 10.63% were achieved for the liver, left kidney, right kidney, and stomach, respectively, using the proposed range-clip algorithm. Also, we compared the proposed architecture with original U-net method, and the experimental results demonstrated the superiority of our approach.

Early Detection of Low Cognitive Scores from Dual-task Performance Data Using a Spatio-temporal Graph Convolutional Neural Network.

Detecting low cognitive scores at an early stage is important for delaying the progress of dementia. Investigations of early-stage detection have employed automatic assessment using dual-task (i.e., performing two different tasks simultaneously). However, current approaches to dual-task-based detection are based on either simple features or limited motion information, which degrades the detection accuracy. To address this problem, we proposed a framework that uses graph convolutional networks to extract spatio-temporal features from dual-task performance data. Moreover, to make the proposed method robust against data imbalance, we devised a loss function that directly optimizes the summation of the sensitivity and specificity of the detection of low cognitive scores (i.e., score≤ 23 or score≤ 27). Our evaluation is based on 171 subjects from 6 different senior citizens' facilities. Our experimental results demonstrated that the proposed algorithm considerably outperforms the previous standard with respect to both the sensitivity and specificity of the detection of low cognitive scores.

X-ray2Shape: Reconstruction of 3D Liver Shape from a Single 2D Projection Image.

Computed tomography (CT) and magnetic resonance imaging (MRI) scanners measure three-dimensional (3D) images of patients. However, only low-dimensional local two-dimensional (2D) images may be obtained during surgery or radiotherapy. Although computer vision techniques have shown that 3D shapes can be estimated from multiple 2D images, shape reconstruction from a single 2D image such as an endoscopic image or an X-ray image remains a challenge. In this study, we propose X-ray2Shape, which permits a deep learning-based 3D organ mesh to be reconstructed from a single 2D projection image. The method learns the mesh deformation from a mean template and deep features computed from the individual projection images. Experiments with organ meshes and digitally reconstructed radiograph (DRR) images of abdominal regions were performed to confirm the estimation performance of the methods.

Continuous lung region segmentation from endoscopic images for intra-operative navigation.

Although preoperative Computed tomography images are widely used in intraoperative navigation, they can not provide precise information for organs such as the lungs, which deform severely during surgery because of deflation. By segmenting lung regions using intraoperative endoscopic images, a more accurate navigation can be obtained because endoscopic images directly provide real-time organ descriptions. However, satisfactory segmentation is rarely achieved with the algorithms in the literature due to the high deformability of the lungs and similarity between the background and object. This article addresses these problems by describing a novel approach for lung region segmentation based on endoscopic images. The proposed method leverages both GrabCut and optical flow for continuous segmentation. It also introduces a novel technique for quick user interaction, in which users are required to quickly provide a rough curve that shows the possible area of the boundary, and then a much more precise segmentation is deduced based on the rough curve. The effectiveness of the proposed approach was demonstrated by comparing it with conventional algorithms. The results show that the average F-measure of the proposed method is more than 97%. The position, size, and boundary of the lungs obtained by the proposed method can provide useful intraoperative navigation for lung resection surgeries.