Medical image segmentation is one of the crucial tasks in diagnosis as well as pre-surgery. Recently, deep learning has significantly contributed to improving the efficiency of medical image extraction. The U-Net network has been a favored network model, which has been used as a platform architecture, for medical image segmentation. For the success of these studies, most of these models were primarily focused on the changing of the interconnection between the nodes in the network, and changing the structure of the convolution units. This would result in the ignorance of the output features of convolution units in the nodes. In this study, a U ⁿ -Net, an n-fold network architecture, was proposed based on the traditional U-Net. In the U ⁿ -Net model, the output features of the convolution units are taken as the skip connection. Therefore, the U ⁿ -Net network exploits the output features of the convolution units in the nodes. In this study, we investigated a U ² -Net and a U ³ -Net for segmentation of the liver and liver tumors. Besides, dilated convolution (DC) and dense structure were also used in the nodes of our networks. The efficiency of our models was evaluated on two public datasets: LiTS and 3DIRCADb. The Dice’s Similarity Coefficient (DSC) of our proposed models achieved 96.38% for liver segmentation and 73.69% for tumor segmentation on the LiTS dataset. For the 3DIRCADb dataset, the results achieved 96.45% in DSC for the liver segmentation and 73.34% for the tumor segmentation. The experimental results show that our proposed networks achieved better results than the recent networks. And it is convinced that our network would be useful for practical deployments.

Deep learning is one of the most effective approaches to medical image processing applications. Network models are being studied more and more for medical image segmentation challenges. The encoder–decoder structure is achieving great success, in particular the Unet architecture, which is used as a baseline architecture for the medical image segmentation networks. Traditional Unet and Unet-based networks still have a limitation that is not able to fully exploit the output features of the convolutional units in the node. In this study, we proposed a new network model named TMD-Unet, which had three main enhancements in comparison with Unet: (1) modifying the interconnection of the network node, (2) using dilated convolution instead of the standard convolution, and (3) integrating the multi-scale input features on the input side of the model and applying a dense skip connection instead of a regular skip connection. Our experiments were performed on seven datasets, including many different medical image modalities such as colonoscopy, electron microscopy (EM), dermoscopy, computed tomography (CT), and magnetic resonance imaging (MRI). The segmentation applications implemented in the paper include EM, nuclei, polyp, skin lesion, left atrium, spleen, and liver segmentation. The dice score of our proposed models achieved 96.43% for liver segmentation, 95.51% for spleen segmentation, 92.65% for polyp segmentation, 94.11% for EM segmentation, 92.49% for nuclei segmentation, 91.81% for left atrium segmentation, and 87.27% for skin lesion segmentation. The experimental results showed that the proposed model was superior to the popular models for all seven applications, which demonstrates the high generality of the proposed model.

Colorectal cancer is a dangerous disease with a high mortality rate. To increase the likelihood of successful treatment, early detection of polyps is a useful solution. The Unet-architecture network model is showing success in medical image segmentation including analysis of polyps from colonoscopy images. Traditional Unet and Unet-based models are often huge, requiring training and deployment with a high-performance system. Designing models with compact size and high-performance would be an important goal. In this study, we proposed to modify the Residual Recurrent Unet architecture to improve the size of the model while ensuring the model performance. The proposed model has flexibility in changing the number of filters in convolution units. By taking advantage of the strengths of residual and recurrent structures in terms of reuse of convolutional functions, the new model, therefore, was not only smaller in size but also has superior performance compared to the traditional Unet model and the others. The evaluations were performed on three public Colonoscopy image datasets: CVC-ClinicDB, ETIS-LaribPolypDB, and CVC-ColonDB. The Dice score on CVC-ClinicDB reached 94.59%, ETIS-LaribPolypDB reached 92.73%, and 93.31% on CVC-ColonDB dataset. The experimental results obtained from the proposed network on datasets were better than those in recent related studies. The introduced model has a smaller size than the traditional model nevertheless has outstanding performance, therefore, it would be extremely productive for developing applications on low-performance devices.

One of the major challenges for Minimally Invasive Surgery (MIS) is the limited field of vision (FOV) of the endoscope. A previous study by the authors designed a MIS Panoramic Endoscope (MISPE) that gives the physician a broad field of view, but this approach is still limited, in terms of performance and quality because it encounters difficulty when there is smoke, specular reflections, or a change in viewpoint. This study proposes a novel algorithm that increases the MISPE’s performance. The method calculates the disparity for the region that is overlapped by the two cameras to allow image stitching. An improved evaluation of the homography matrix uses a frame-by-frame calculation, so the stitched videos are more stable for MIS. The experimental results show that the revised MISPE has a FOV that is 55% greater, and the system operates stably in real time. The proposed system allows a frame rate of 26.7 fps on a single CPU computer. The proposed stitching method is 1.55 times faster than the previous method. The stitched image that is obtained using the proposed method is as similar as the ground truth as the SURF-based stitching method that was used in the previous study.

The integration of technology into pharmaceutical operations has led to the development of automated drug delivery systems, bringing numerous benefits such as reducing medication errors and improving patient satisfaction. With advancements in technology, automated drug delivery systems have a huge growth potential. Their deployment can significantly improve healthcare services and drive the development of the pharmaceutical industry. In this study, an embedded system on Raspberry Pi integrated with the YOLOv5 deep learning model and a hardware system controlled by a Mitsubishi FX5U Programmable Logic Controller (PLC) is proposed for a drug dispensing system. Drug vials will be collected and their images analyzed by YOLOv5, and a proposed line cutting position determination algorithm will identify the necessary cutting positions. These positions will be communicated to the PLC and control the cutting system accordingly. The training results of the YOLOv5 model achieved an accuracy of over 99% for basic drug types. The optimal cutting path determination algorithm provides the correct cutting positions to the cutting system from the PLC. The research results contribute to the construction and development of automated drug dispensing devices and systems.