Assessing the location and extent of lesions caused by chronic stroke is critical for medical diagnosis, surgical planning, and prognosis. In recent years, with the rapid development of 2D and 3D convolutional neural networks (CNN), the encoder-decoder structure has shown great potential in the field of medical image segmentation. However, the 2D CNN ignores the 3D information of medical images, while the 3D CNN suffers from high computational resource demands. This paper proposes a new architecture called dimension-fusion-UNet (D-UNet), which combines 2D and 3D convolution innovatively in the encoding stage. The proposed architecture achieves a better segmentation performance than 2D networks, while requiring significantly less computation time in comparison to 3D networks. Furthermore, to alleviate the data imbalance issue between positive and negative samples for the network training, we propose a new loss function called Enhance Mixing Loss (EML). This function adds a weighted focal coefficient and combines two traditional loss functions. The proposed method has been tested on the ATLAS dataset and compared to three state-of-the-art methods. The results demonstrate that the proposed method achieves the best quality performance in terms of $\mathrm{DSC} = 0.5349 \pm 0.2763$ and $\mathrm{precision} = 0.6331 \pm 0.295$ ).

This paper proposes a segmentation-free radiomics method to classify malignant and benign breast tumors with shear-wave elastography (SWE) data. The method is targeted to integrate the advantage of both SWE in providing important elastic with morphology information and convolutional neural network (CNN) in automatic feature extraction and accurate classification. Compared to traditional methods, the proposed method is designed to directly extract features from the dataset without the pre-requisite of segmentation and manual operation. This can keep the peri-tumor information which is lost by segmentation-based methods. With the proposed model trained on 205 cases (70 from malignant breast patients and 135 from benign breast patients respectively), an accuracy of 95.79%, a sensitivity of 96.23% and a specificity of 95.65% was obtained for the final test. The superior performances compared to the existing state-of-the-art methods and its automatic nature both demonstrate that the proposed method has a great potential to be applied to clinical computer-aided diagnosis of breast cancer.

Muscle fascicle pennation angle (PA) is an important parameter related to musculoskeletal functions, and ultrasound imaging has been widely used for measuring PA, but manually and frame by frame in most cases. We have earlier reported an automatic method to estimate aponeurosis orientation based on Gabor transform and Revoting Hough Transform (RVHT). In this paper, we proposed a method to estimate the overall orientation of muscle fascicles in a region of interest, in order to complete computing the orientation of the other side of the pennation angle, but the side found by RVHT. The measurements for orientations of both fascicles and aponeurosis were conducted in each frame of ultrasound images, and then the dynamic change of pennation angle during muscle contraction was obtained automatically. The method for fascicle orientation estimation was evaluated using synthetic images with different noise levels and later on 500 ultrasound images of human gastrocnemius muscles during isometric plantarflexion. The muscle fascicle orientations were also estimated manually by two operators. From the results it’s found that the proposed automatic method demonstrated a comparable performance to the manual method. With the proposed methods, ultrasound measurement for muscle pennation angles can be more widely used for functional assessment of muscles.

An accurate segmentation and quantification of the superficial foveal avascular zone (sFAZ) is important to facilitate the diagnosis and treatment of many retinal diseases, such as diabetic retinopathy and retinal vein occlusion. We proposed a method based on deep learning for the automatic segmentation and quantification of the sFAZ in optical coherence tomography angiography (OCTA) images with robustness to brightness and contrast (B/C) variations. A dataset of 405 OCTA images from 45 participants was acquired with Zeiss Cirrus HD-OCT 5000 and the ground truth (GT) was manually segmented subsequently. A deep learning network with an encoder–decoder architecture was created to classify each pixel into an sFAZ or non-sFAZ class. Subsequently, we applied largest-connected-region extraction and hole-filling to fine-tune the automatic segmentation results. A maximum mean dice similarity coefficient (DSC) of 0.976 ± 0.011 was obtained when the automatic segmentation results were compared against the GT. The correlation coefficient between the area calculated from the automatic segmentation results and that calculated from the GT was 0.997. In all nine parameter groups with various brightness/contrast, all the DSCs of the proposed method were higher than 0.96. The proposed method achieved better performance in the sFAZ segmentation and quantification compared to two previously reported methods. In conclusion, we proposed and successfully verified an automatic sFAZ segmentation and quantification method based on deep learning with robustness to B/C variations. For clinical applications, this is an important progress in creating an automated segmentation and quantification applicable to clinical analysis.

This paper analyzes the current status, technological developments, academic exchange platforms, and future challenges and solutions in the field of intelligent ophthalmology (IO) in China. In terms of technology, significant progress has been made in various areas, including diabetic retinopathy, fundus image analysis, quality assessment of medical artificial intelligence products, clinical research methods, technical evaluation, and industry standards. Researchers continually enhance the safety and standardization of IO technology by formulating a series of clinical application guidelines and standards. The establishment of domestic and international academic exchange platforms provides extensive collaboration opportunities for professionals in various fields, and various academic journals serve as publication platforms for IO research. However, challenges such as technological innovation, data privacy and security, lagging regulations, and talent shortages still pose obstacles to future development. To address these issues, future efforts should focus on strengthening technological research and development, regulatory framework construction, talent cultivation, and increasing patient awareness and acceptance of new technologies. By comprehensively addressing these challenges, IO in China is poised to further lead the industry's development on a global scale, bringing more innovation and convenience to the field of ophthalmic healthcare.