Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models

Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation (e.g. the general data protection regulation (GDPR)). Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing as GDPR should not apply for medical images which do not belong to a specific person. However, in order to share synthetic images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1-3) and a diffusion model for the task of brain tumor segmentation. Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80\% - 90\% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small. Furthermore, we demonstrate that common metrics for evaluating synthetic images, Fr\'echet inception distance (FID) and inception score (IS), do not correlate well with the obtained performance when using the synthetic images for training segmentation networks.Comment: 20 Pages. arXiv admin note: text overlap with arXiv:2211.0408

Beware of diffusion models for synthesizing medical images -- A comparison with GANs in terms of memorizing brain MRI and chest x-ray images

Diffusion models were initially developed for text-to-image generation and are now being utilized to generate high-quality synthetic images. Preceded by GANs, diffusion models have shown impressive results using various evaluation metrics. However, commonly used metrics such as FID and IS are not suitable for determining whether diffusion models are simply reproducing the training images. Here we train StyleGAN and diffusion models, using BRATS20, BRATS21 and a chest x-ray pneumonia dataset, to synthesize brain MRI and chest x-ray images, and measure the correlation between the synthe4c images and all training images. Our results show that diffusion models are more likely to memorize the training images, compared to StyleGAN, especially for small datasets and when using 2D slices from 3D volumes. Researchers should be careful when using diffusion models for medical imaging, if the final goal is to share the synthe4c imagesComment: 12 Pages, 6 Figure

Does an ensemble of GANs lead to better performance when training segmentation networks with synthetic images?

Large annotated datasets are required to train segmentation networks. In medical imaging, it is often difficult, time consuming and expensive to create such datasets, and it may also be difficult to share these datasets with other researchers. Different AI models can today generate very realistic synthetic images, which can potentially be openly shared as they do not belong to specific persons. However, recent work has shown that using synthetic images for training deep networks often leads to worse performance compared to using real images. Here we demonstrate that using synthetic images and annotations from an ensemble of 10 GANs, instead of from a single GAN, increases the Dice score on real test images with 4.7 % to 14.0 % on specific classes.Comment: 5 pages, submitted to ISBI 202

Digital Twin Concept, Method and Technical Framework for Smart Meters

Smart meters connect smart grid electricity suppliers and users. Smart meters have become a research hotspot as smart grid applications like demand response, power theft prevention, power quality monitoring, peak valley time of use prices, and peer-to-peer (P2P) energy trading have grown. But, as the carriers of these functions, smart meters have technical problems such as limited computing resources, difficulty in upgrading, and high costs, which to some extent restrict the further development of smart grid applications. To address these issues, this study offers a container-based digital twin (CDT) approach for smart meters, which not only increases the user-facing computing resources of smart meters but also simplifies and lowers the overall cost and technical complexity of meter changes. In order to further validate the effectiveness of this method in real-time applications on the smart grid user side, this article tested and analyzed the communication performance of the digital twin system in three areas: remote application services, peer-to-peer transactions, and real-time user request services. The experimental results show that the CDT method proposed in this paper meets the basic requirements of smart grid user-side applications for real-time communication. The container is deployed in the cloud, and the average time required to complete 100 P2P communications using our smart meter structure is less than 2.4 seconds, while the average time required for existing smart meter structures to complete the same number of P2P communications is 208 seconds. Finally, applications, the future development direction of the digital twin method, and technology architecture are projected

Energy Efficiency in Transportation along with the Belt and Road Countries

China’s huge investment in the “belt and road initiative” (BRI) may have helped improve the economic level of participating countries, but it may also be accompanied by a substantial increase in greenhouse gas (GHG) emissions. The BRI corridors aim to bring regional stability and prosperity. In such efforts, energy efficiency due to increased transport has been overlooked in the recent literature. This paper employed a data envelopment analysis of the slack-based measurement (SBM) for bad output to assess the transport energy efficiency of 19 countries under the BRI economic corridors. By using the most cited transport-related input variables, such as vehicles, labor, motor oil, jet fuel, and natural gas, this study first analyzes the transport energy efficiency by first assuming the output variables individually and then takes two years as a pre- and post-BRI case by considering the aggregated output model. The results show an increase in economic activity but a decline in transport energy efficiency in terms of consumption and emissions

Frequency of Post-Operative Fecal Incontinence and Healing Rate in Patients with Open and Closed Lateral Internal Anal Sphincterotomy

OBJECTIVES This study aims to compare the frequency of fecal incontinence and healing rate in patients treated with Open Internal Anal Sphincterotomy (OIAS) and Closed Lateral Internal Anal Sphincterotomy (CLIAS). METHODOLOGY This randomized control trial was carried out in the Department of Surgery Hayatabad Medical Complex, Peshawar Pakistan from Feb 2019 to Feb 2020. Eighty-four patients were assigned to the open method in Group B while eighty-four patients were allocated to the closed method in Group A (using blade 11) through the randomized control trial method. Fecal incontinence and healing rate were observed on the 7 th postoperative day. SPSS 23.0 software was used to analyse the data. RESULTS In group A out of a total of 84 patients, 96% of patients were in category A, 4% of patients were in category B, and no patients were in categories C and D. In group B out of 84 patients, 96% of patients were in category A while 4% patients were in category B and no patients were in category C and D. The total faecal incontinence in Group B (open method) was 21% while total fecal incontinence in Group A (closed method) was only 4% that is a clear dierence between the two groups. In group A (closed method) out of 84 patients, 2 patients (2.38 %) showed delayed healing while 82 patients (97.61 %) showed normal healing. In group B (open method) 7.4% of patients showed delayed healing with a signicance p level of 0.04 while 92.85 showed normal healing. CONCLUSION Fecal incontinence was less in closed Lateral Anal Sphincterotomy due to the use of blade 11 while it was higher in open internal anal sphincterotomy. Similarly, the healing rate was signicantly higher in the closed method while delayed healing was seen in the open method

Segmentation of Kidney and Tumor using Auxiliary Information

Automatic segmentation of organs and tumors is a prerequisite of many clinical application in radiology. The anatomical variability of organs in the abdomen and especially of tumors makes it difficult for many methods to obtain good segmentations. in this report we present a cascade of two convolutional neural networks allowing to segment an organ followed by the segmentation of a tumor. The advantage of the proposed pipeline is that the preliminary organ segmentation, which is a simpler task, helps the further segmentation of the tumor. The proposed system was evaluated using the KiTS19 challange dataset

Efficient brain age prediction from 3D MRI volumes using 2D projections

Using 3D CNNs on high resolution medical volumes is very computationally demanding, especially for large datasets like the UK Biobank which aims to scan 100,000 subjects. Here we demonstrate that using 2D CNNs on a few 2D projections (representing mean and standard deviation across axial, sagittal and coronal slices) of the 3D volumes leads to reasonable test accuracy when predicting the age from brain volumes. Using our approach, one training epoch with 20,324 subjects takes 20 - 50 seconds using a single GPU, which two orders of magnitude faster compared to a small 3D CNN. These results are important for researchers who do not have access to expensive GPU hardware for 3D CNNs

Clinicopathological prognostic factors of oral squamous cell carcinoma: An experience of a tertiary care hospital

Locoregional recurrence accounts for majority of the treatment failures in oral cancer patients. Current study aimed to determine the predictors of recurrence and survival in patients with biopsy proven Squamous Cell Carcinoma (SCC) of the oral cavity. This study included 88 patients of squamous cell carcinoma treated at our institution from 2007 till 2013. Primary intervention was surgery in all patients with radiation and chemotherapy in selected patients. Primary end point was locoregional recurrence, distant metastasis and death. Out of 88 patients, 23 (26.1%) patients developed locoregional recurrence and 6 (6.8%) patients developed distant metastasis. Overall survival rate was 77.3%. Follow up ranged from 1 month to 63 months with mean of 17.8±16.2. On multivariate analysis, lymph node involvement and loco-regional recurrence were independent parameters related to decrease overall survival. Lymphovascular invasion, perineural spread, TNM stage and lymph node involvement had significant impact on recurrence

Impact of diabetes mellitus on nerves

Background: Involvement of the peripheral and autonomic nervous systems is probably the most common complication of diabetes. The main symptoms of diabetic polyneuropathy include negative symptoms (those related to nerve fiber loss or dysfunction) such as numbness and weakness, and positive symptoms (those related to abnormal function of surviving nerve fibers) such as tingling and pain.Methods: This was a cross-sectional study held in diabetic clinic of Nishter hospital, Multan, Pakistan. The study included any diabetic patients showing symptoms of neuropathy.Results: There were total of 140 in this study. This study included 85% of male and 15% of female. Most common symptoms of diabetic neuropathy were pain (70%) and tingling (70%) followed by numbness in 65% of patients. There were 28 patients in 5 years duration of diabetes, 35 people in 6-10 years duration, 21 patients in 11-15 years duration, and 14 patients in 20+ years duration.Conclusions: Neuropathy due to diabetes is crippling especially when pain is the prominent symptoms. Autonomic symptoms like constipation and lightheadedness are discomforting for the patients. The most commonly used screening test is vibrating tuning fork test which is east to perform is clinical setting and is not time consuming. Diabetic patients need to take special care of