The growing interest in Subseasonal to Seasonal (S2S) prediction data across different industries underscores its potential use in comprehending weather patterns, extreme conditions, and important sectors such as agriculture and energy management. However, concerns about its accuracy have been raised. Furthermore, enhancing the precision of rainfall predictions remains challenging in S2S forecasts. This study enhanced the sub-seasonal to seasonal (S2S) prediction skills for precipitation amount and occurrence over the East Asian region by employing deep learning-based post-processing techniques. We utilized a modified U-Net architecture that wraps all its convolutional layers with TimeDistributed layers as a deep learning model. For the training datasets, the precipitation prediction data of six S2S climate models and their multi-model ensemble (MME) were constructed, and the daily precipitation occurrence was obtained from the three thresholds values, 0 % of the daily precipitation for no-rain events, <33 % for light-rain, >67 % for heavy-rain. Based on the precipitation amount prediction skills of the six climate models, deep learning-based post-processing outperformed post-processing using multiple linear regression (MLR) in the lead times of weeks 2-4. The prediction accuracy of precipitation occurrence with MLR-based post-processing did not significantly improve, whereas deep learning-based post-processing enhanced the prediction accuracy in the total lead times, demonstrating superiority over MLR. We enhanced the prediction accuracy in forecasting the amount and occurrence of precipitation in individual climate models using deep learning-based post-processing.

OBJECTIVE: As metabolic dysfunction-associated steatotic liver disease (MASLD) becomes more prevalent worldwide, it is imperative to create more accurate technologies that make it easy to assess the liver in a point-of-care setting. The aim of this study is to test the performance of a new software tool implemented in Velacur (Sonic Incytes), a liver stiffness and ultrasound attenuation measurement device, on patients with MASLD. This tool employs a deep learning-based method to detect and segment shear waves in the liver tissue for subsequent analysis to improve tissue characterization for patient diagnosis.
METHODS: This new tool consists of a deep learning based algorithm, which was trained on 15,045 expert-segmented images from 103 patients, using a U-Net architecture. The algorithm was then tested on 4429 images from 36 volunteers and patients with MASLD. Test subjects were scanned at different clinics with different Velacur operators. Evaluation was performed on both individual images (image based) and averaged across all images collected from a patient (patient based). Ground truth was defined by expert segmentation of the shear waves within each image. For evaluation, sensitivity and specificity for correct wave detection in the image were calculated. For those images containing waves, the Dice coefficient was calculated. A prototype of the software tool was also implemented on Velacur and assessed by operators in real world settings.
RESULTS: The wave detection algorithm had a sensitivity of 81% and a specificity of 84%, with a Dice coefficient of 0.74 and 0.75 for image based and patient-based averages respectively. The implementation of this software tool as an overlay on the B-Mode ultrasound resulted in improved exam quality collected by operators.
CONCLUSIONS: The shear wave algorithm performed well on a test set of volunteers and patients with metabolic dysfunction-associated steatotic liver disease. The addition of this software tool, implemented on the Velacur system, improved the quality of the liver assessments performed in a real world, point of care setting.

UNASSIGNED: Liver cancer poses a significant health challenge due to its high incidence rates and complexities in detection and treatment. Accurate segmentation of liver tumors using medical imaging plays a crucial role in early diagnosis and treatment planning.
UNASSIGNED: This study proposes a novel approach combining U-Net and ResNet architectures with the Adam optimizer and sigmoid activation function. The method leverages ResNet\'s deep residual learning to address training issues in deep neural networks. At the same time, U-Net\'s structure facilitates capturing local and global contextual information essential for precise tumor characterization. The model aims to enhance segmentation accuracy by effectively capturing intricate tumor features and contextual details by integrating these architectures. The Adam optimizer expedites model convergence by dynamically adjusting the learning rate based on gradient statistics during training.
UNASSIGNED: To validate the effectiveness of the proposed approach, segmentation experiments are conducted on a diverse dataset comprising 130 CT scans of liver cancers. Furthermore, a state-of-the-art fusion strategy is introduced, combining the robust feature learning capabilities of the UNet-ResNet classifier with Snake-based Level Set Segmentation.
UNASSIGNED: Experimental results demonstrate impressive performance metrics, including an accuracy of 0.98 and a minimal loss of 0.10, underscoring the efficacy of the proposed methodology in liver cancer segmentation.
UNASSIGNED: This fusion approach effectively delineates complex and diffuse tumor shapes, significantly reducing errors.

OBJECTIVE: A fully automated laminar cartilage composition (MRI-based T2) analysis method was technically and clinically validated by comparing radiographically normal knees with (CL-JSN) and without contra-lateral joint space narrowing or other signs of radiographic osteoarthritis (OA, CL-noROA).
METHODS: 2D U-Nets were trained from manually segmented femorotibial cartilages (n = 72) from all 7 echoes (AllE), or from the 1st echo only (1stE) of multi-echo-spin-echo (MESE) MRIs acquired by the Osteoarthritis Initiative (OAI). Because of its greater accuracy, only the AllE U-Net was then applied to knees from the OAI healthy reference cohort (n = 10), CL-JSN (n = 39), and (1:1) matched CL-noROA knees (n = 39) that all had manual expert segmentation, and to 982 non-matched CL-noROA knees without expert segmentation.
RESULTS: The agreement (Dice similarity coefficient) between automated vs. manual expert cartilage segmentation was between 0.82 ± 0.05/0.79 ± 0.06 (AllE/1stE) and 0.88 ± 0.03/0.88 ± 0.03 (AllE/1stE) across femorotibial cartilage plates. The deviation between automated vs. manually derived laminar T2 reached up to - 2.2 ± 2.6 ms/ + 4.1 ± 10.2 ms (AllE/1stE). The AllE U-Net showed a similar sensitivity to cross-sectional laminar T2 differences between CL-JSN and CL-noROA knees in the matched (Cohen\'s D ≤ 0.54) and the non-matched (D ≤ 0.54) comparison as the matched manual analyses (D ≤ 0.48). Longitudinally, the AllE U-Net also showed a similar sensitivity to CL-JSN vs. CS-noROA differences in the matched (D ≤ 0.51) and the non-matched (D ≤ 0.43) comparison as matched manual analyses (D ≤ 0.41).
CONCLUSIONS: The fully automated T2 analysis showed a high agreement, acceptable accuracy, and similar sensitivity to cross-sectional and longitudinal laminar T2 differences in an early OA model, compared with manual expert analysis.
BACKGROUND: Clinicaltrials.gov identification: NCT00080171.

Echocardiography is one the most commonly used imaging modalities for the diagnosis of congenital heart disease. Echocardiographic image analysis is crucial to obtaining accurate cardiac anatomy information. Semantic segmentation models can be used to precisely delimit the borders of the left ventricle, and allow an accurate and automatic identification of the region of interest, which can be extremely useful for cardiologists. In the field of computer vision, convolutional neural network (CNN) architectures remain dominant. Existing CNN approaches have proved highly efficient for the segmentation of various medical images over the past decade. However, these solutions usually struggle to capture long-range dependencies, especially when it comes to images with objects of different scales and complex structures. In this study, we present an efficient method for semantic segmentation of echocardiographic images that overcomes these challenges by leveraging the self-attention mechanism of the Transformer architecture. The proposed solution extracts long-range dependencies and efficiently processes objects at different scales, improving performance in a variety of tasks. We introduce Shifted Windows Transformer models (Swin Transformers), which encode both the content of anatomical structures and the relationship between them. Our solution combines the Swin Transformer and U-Net architectures, producing a U-shaped variant. The validation of the proposed method is performed with the EchoNet-Dynamic dataset used to train our model. The results show an accuracy of 0.97, a Dice coefficient of 0.87, and an Intersection over union (IoU) of 0.78. Swin Transformer models are promising for semantically segmenting echocardiographic images and may help assist cardiologists in automatically analyzing and measuring complex echocardiographic images.

The rat sciatic nerve model is commonly used to test novel therapies for nerve injury repair. The static sciatic index (SSI) is a useful metric for quantifying functional recovery, and involves comparing an operated paw versus a control paw using a weighted ratio between the toe spread and the internal toe spread. To calculate it, rats are placed in a transparent box, photos are taken from underneath and the toe distances measured manually. This is labour intensive and subject to human error due to the challenge of consistently taking photos, identifying digits and making manual measurements. Although several commercial kits have been developed to address this challenge, they have seen little dissemination due to cost. Here we develop a novel algorithm for automatic measurement of SSI metrics based on video data using casted U-Nets. The algorithm consists of three U-Nets, one to segment the hind paws and two for the two pairs of digits which input into the SSI calculation. A training intersection over union error of 60 % and 80 % was achieved for the back paws and for both digit segmentation U-Nets, respectfully. The algorithm was tested against video data from three separate experiments. Compared to manual measurements, the algorithm provides the same profile of recovery for every experiment but with a tighter standard deviation in the SSI measure. Through the open-source release of this algorithm, we aim to provide an inexpensive tool to more reliably quantify functional recovery metrics to the nerve repair research community.

Post-mortem (PM) imaging has potential for identifying individuals by comparing ante-mortem (AM) and PM images. Radiographic images of bones contain significant information for personal identification. However, PM images are affected by soft tissue decomposition; therefore, it is desirable to extract only images of bones that change little over time. This study evaluated the effectiveness of U-Net for bone image extraction from two-dimensional (2D) X-ray images. Two types of pseudo 2D X-ray images were created from the PM computed tomography (CT) volumetric data using ray-summation processing for training U-Net. One was a projection of all body tissues, and the other was a projection of only bones. The performance of the U-Net for bone extraction was evaluated using Intersection over Union, Dice coefficient, and the area under the receiver operating characteristic curve. Additionally, AM chest radiographs were used to evaluate its performance with real 2D images. Our results indicated that bones could be extracted visually and accurately from both AM and PM images using U-Net. The extracted bone images could provide useful information for personal identification in forensic pathology.

Accurate and efficient segmentation of coronary arteries from CTA images is crucial for diagnosing and treating cardiovascular diseases. This study proposes a structured approach that combines vesselness enhancement, heart region of interest (ROI) extraction, and the ResUNet deep learning method to accurately and efficiently extract coronary artery vessels. Vesselness enhancement and heart ROI extraction significantly improve the accuracy and efficiency of the segmentation process, while ResUNet enables the model to capture both local and global features. The proposed method outperformed other state-of-the-art methods, achieving a Dice similarity coefficient (DSC) of 0.867, a Recall of 0.881, and a Precision of 0.892. The exceptional results for segmenting coronary arteries from CTA images demonstrate the potential of this method to significantly contribute to accurate diagnosis and effective treatment of cardiovascular diseases.

Liver segmentation technologies play vital roles in clinical diagnosis, disease monitoring, and surgical planning due to the complex anatomical structure and physiological functions of the liver. This paper provides a comprehensive review of the developments, challenges, and future directions in liver segmentation technology. We systematically analyzed high-quality research published between 2014 and 2024, focusing on liver segmentation methods, public datasets, and evaluation metrics. This review highlights the transition from manual to semi-automatic and fully automatic segmentation methods, describes the capabilities and limitations of available technologies, and provides future outlooks.

Jaw cyst is a fluid-containing cystic lesion that can occur in any part of the jaw and cause facial swelling, dental lesions, jaw fractures, and other associated issues. Due to the diversity and complexity of jaw images, existing deep-learning methods still have challenges in segmentation. To this end, we propose MARes-Net, an innovative multi-scale attentional residual network architecture. Firstly, the residual connection is used to optimize the encoder-decoder process, which effectively solves the gradient disappearance problem and improves the training efficiency and optimization ability. Secondly, the scale-aware feature extraction module (SFEM) significantly enhances the network\'s perceptual abilities by extending its receptive field across various scales, spaces, and channel dimensions. Thirdly, the multi-scale compression excitation module (MCEM) compresses and excites the feature map, and combines it with contextual information to obtain better model performance capabilities. Furthermore, the introduction of the attention gate module marks a significant advancement in refining the feature map output. Finally, rigorous experimentation conducted on the original jaw cyst dataset provided by Quzhou People\'s Hospital to verify the validity of MARes-Net architecture. The experimental data showed that precision, recall, IoU and F1-score of MARes-Net reached 93.84%, 93.70%, 86.17%, and 93.21%, respectively. Compared with existing models, our MARes-Net shows its unparalleled capabilities in accurately delineating and localizing anatomical structures in the jaw cyst image segmentation.