MCSE-U-Net: multi-convolution blocks and squeeze and excitation blocks for vessel segmentation.

Background: Capturing the segmentation of blood vessels by a fundus camera is crucial for the medical evaluation of various retinal vascular issues. However, due to the complicated vascular structure and unclear clinical criteria, the precise segmentation of blood arteries remains very challenging. Methods: To address this issue, we developed the upgraded multi-convolution block and squeeze and excitation based on the U-shape network (MCSE-U-net) model that segments retinal vessels using a U-shaped network. This model uses multi-convolution (MC) blocks, squeeze and excitation (SE) blocks, and squeeze blocks. First, the input image was processed using the luminance, chrominance-blue, chrominance-red (YCbCr) color conversion method to further improve visibility. Second, a MC module was added to increase the model's ability to accurately segment blood vessels. Third, SE blocks were added to enhance the network model's ability to segment fine blood vessels in medical images. Results: The suggested architecture was assessed using evaluation metrics, including the Dice coefficient, sensitivity (sen), specificity (spe), accuracy (acc), and mean intersection over union (mIoU), on an open-source Digital Retinal Images for Vessel Extraction (DRIVE) data set. The outcomes showed the effectiveness of the suggested approach, particularly in the extraction of peripheral vascular anatomy. Using the suggested architecture, the model had a Dice coefficient of 0.8430, a sen of 0.8752, a spe of 0.9902, an acc of 0.9725, and a mIoU of 0.8473 for the DRIVE data set. The Dice coefficient, sen, spe, acc, and mIoU of the MCSE-U-net increased by 3.08%, 6.22%, 0.62%, 0.61%, and 3.01%, respectively, compared to the original U-net, demonstrating the better all-around performance of the MCSE-U-net. Conclusions: The MCSE-U-net network performed and achieved more than the technologies already in use.

GAM-SpCaNet: Gradient awareness minimization-based spinal convolution attention network for brain tumor classification.

Brain tumor is one of the common diseases of the central nervous system, with high morbidity and mortality. Due to the wide range of brain tumor types and pathological types, the same type is divided into different subgrades. The imaging manifestations are complex, making clinical diagnosis and treatment difficult. In this paper, we construct SpCaNet (Spinal Convolution Attention Network) to effectively utilize the pathological features of brain tumors, consisting of a Positional Attention (PA) convolution block, Relative self-attention transformer block, and Intermittent fully connected (IFC) layer. Our method is more lightweight and efficient in recognition of brain tumors. Compared with the SOTA model, the number of parameters is reduced by more than three times. In addition, we propose the gradient awareness minimization (GAM) algorithm to solve the problem of insufficient generalization ability of the traditional Stochastic Gradient Descent (SGD) method and use it to train the SpCaNet model. Compared with SGD, GAM achieves better classification performance. According to the experimental results, our method has achieved the highest accuracy of 99.28%, and the proposed method performs well in classifying brain tumors.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) quantification of transient ischemia using a combination method of 5-pool Lorentzian fitting and inverse Z-spectrum analysis.

Background: Chemical exchange saturation transfer (CEST) is a promising method for the detection of biochemical alterations in cancers and neurological diseases. However, the sensitivity of the currently existing quantitative method for detecting ischemia needs further improvement. Methods: To further improve the quantification of the CEST signal and enhance the CEST detection for ischemia, we used a quantitative analysis method that combines an inverse Z-spectrum analysis and a 5-pool Lorentzian fitting. Specifically, a 5-pool Lorentzian simulation was conducted with the following brain tissue parameters: water, amide (3.5 ppm), amine (2.2 ppm), magnetization transfer (MT), and nuclear Overhauser enhancement (NOE; -3.5 ppm). The parameters were first calculated offline and stored as the initial value of the Z-spectrum fitting. Then, the measured Z-spectrum with the peak value set to 0 was fitted via the stored initial value, which yielded the reference Z-spectrum. Finally, the difference between the inverse of the Z-spectrum and the inverse of the reference Z-spectrum was used as the CEST definite spectrum. Results: The simulation results demonstrated that the Z-spectra of the rat brain were well simulated by a 5-pool Lorentzian fitting. Further, the proposed method detected a larger difference than did either the saturation transfer difference or the 5-pool Lorentzian fitting, as demonstrated by simulations. According to the results of the cerebral ischemia rat model, the proposed method provided the highest contrast-to-noise ratio (CNR) between the contralateral and the ipsilateral striatum under various acquisition conditions. The results indicated that the difference of fitted amplitudes generated with a 5-pool Lorentzian fitting in amide at 3.5 ppm (6.04%±0.39%; 6.86%±0.39%) was decreased in a stroke lesion compared to the contralateral normal tissue. Moreover, the difference of the residual of inversed Z-spectra in which 5-pool Lorentzian fitting was used to calculate the reference Z-spectra ( M T R R e x 5 L ) amplitudes in amide at 3.5 ppm (13.83%±2.20%, 15.69%±1.99%) was reduced in a stroke lesion compared to the contralateral normal tissue. Conclusions: M T R R e x 5 L is predominantly pH-sensitive and is suitable for detecting tissue acidosis following an acute stroke.