Intelligent analysis and measurement of semicircular canal spatial attitude.

Objective: The primary aim of this investigation was to devise an intelligent approach for interpreting and measuring the spatial orientation of semicircular canals based on cranial MRI. The ultimate objective is to employ this intelligent method to construct a precise mathematical model that accurately represents the spatial orientation of the semicircular canals. Methods: Using a dataset of 115 cranial MRI scans, this study employed the nnDetection deep learning algorithm to perform automated segmentation of the semicircular canals and the eyeballs (left and right). The center points of each semicircular canal were organized into an ordered structure using point characteristic analysis. Subsequently, a point-by-point plane fit was performed along these centerlines, and the normal vector of the semicircular canals was computed using the singular value decomposition method and calibrated to a standard spatial coordinate system whose transverse planes were the top of the common crus and the bottom of the eyeballs. Results: The nnDetection target recognition segmentation algorithm achieved Dice values of 0.9585 and 0.9663. The direction angles of the unit normal vectors for the left anterior, lateral, and posterior semicircular canal planes were [80.19°, 124.32°, 36.08°], [169.88°, 100.04°, 91.32°], and [79.33°, 130.63°, 137.4°], respectively. For the right side, the angles were [79.03°, 125.41°, 142.42°], [171.45°, 98.53°, 89.43°], and [80.12°, 132.42°, 44.11°], respectively. Conclusion: This study successfully achieved real-time automated understanding and measurement of the spatial orientation of semicircular canals, providing a solid foundation for personalized diagnosis and treatment optimization of vestibular diseases. It also establishes essential tools and a theoretical basis for future research into vestibular function and related diseases.

Referring Image Segmentation with Multi-Modal Feature Interaction and Alignment Based on Convolutional Nonlinear Spiking Neural Membrane Systems.

Referring image segmentation aims to accurately align image pixels and text features for object segmentation based on natural language descriptions. This paper proposes NSNPRIS (convolutional nonlinear spiking neural P systems for referring image segmentation), a novel model based on convolutional nonlinear spiking neural P systems. NSNPRIS features NSNPFusion and Language Gate modules to enhance feature interaction during encoding, along with an NSNPDecoder for feature alignment and decoding. Experimental results on RefCOCO, RefCOCO[Formula: see text], and G-Ref datasets demonstrate that NSNPRIS performs better than mainstream methods. Our contributions include advances in the alignment of pixel and textual features and the improvement of segmentation accuracy.

Applying Deep Learning with Convolutional Neural Networks to Laryngoscopic Imaging for Automated Segmentation and Classification of Vocal Cord Leukoplakia.

Objectives: Vocal cord leukoplakia is clinically described as a white plaque or patch on the vocal cords observed during macroscopic examination, which does not take into account histological features or prognosis. A clinical challenge in managing vocal cord leukoplakia is to assess the potential malignant transformation of the lesion. This study aims to investigate the potential of deep learning (DL) for the simultaneous segmentation and classification of vocal cord leukoplakia using narrow band imaging (NBI) and white light imaging (WLI). The primary objective is to assess the model's accuracy in detecting and classifying lesions, comparing its performance in WLI and NBI. Methods: We applied DL to segment and classify NBI and WLI of vocal cord leukoplakia, and used pathological diagnosis as the gold standard. Results: The DL model autonomously detected lesions with an average intersection-over-union (IoU) >70%. In classification tasks, the model differentiated between lesions in the surgical group with a sensitivity of 93% and a specificity of 94% for WLI, and a sensitivity of 99% and a specificity of 97% for NBI. In addition, the model achieved a mean average precision of 81% in WLI and 92% in NBI, with an IoU threshold >0.5. Conclusions: The model proposed by us is helpful in assisting in accurate diagnosis of vocal cord leukoplakia from NBI and WLI.

Development of an Automated Low-Cost Multispectral Imaging System to Quantify Canopy Size and Pigmentation.

Canopy imaging offers a non-destructive, efficient way to objectively measure canopy size, detect stress symptoms, and assess pigment concentrations. While it is faster and easier than traditional destructive methods, manual image analysis, including segmentation and evaluation, can be time-consuming. To make imaging more widely accessible, it's essential to reduce the cost of imaging systems and automate the analysis process. We developed a low-cost imaging system with automated analysis using an embedded microcomputer equipped with a monochrome camera and a filter for a total hardware cost of ~USD 500. Our imaging system takes images under blue, green, red, and infrared light, as well as chlorophyll fluorescence. The system uses a Python-based program to collect and analyze images automatically. The multi-spectral imaging system separates plants from the background using a chlorophyll fluorescence image, which is also used to quantify canopy size. The system then generates normalized difference vegetation index (NDVI, "greenness") images and histograms, providing quantitative, spatially resolved information. We verified that these indices correlate with leaf chlorophyll content and can easily add other indices by installing light sources with the desired spectrums. The low cost of the system can make this imaging technology widely available.

Developing Machine Vision in Tree-Fruit Applications-Fruit Count, Fruit Size and Branch Avoidance in Automated Harvesting.

Recent developments in affordable depth imaging hardware and the use of 2D Convolutional Neural Networks (CNN) in object detection and segmentation have accelerated the adoption of machine vision in a range of applications, with mainstream models often out-performing previous application-specific architectures. The need for the release of training and test datasets with any work reporting model development is emphasized to enable the re-evaluation of published work. An additional reporting need is the documentation of the performance of the re-training of a given model, quantifying the impact of stochastic processes in training. Three mango orchard applications were considered: the (i) fruit count, (ii) fruit size and (iii) branch avoidance in automated harvesting. All training and test datasets used in this work are available publicly. The mAP 'coefficient of variation' (Standard Deviation, SD, divided by mean of predictions using models of repeated trainings × 100) was approximately 0.2% for the fruit detection model and 1 and 2% for the fruit and branch segmentation models, respectively. A YOLOv8m model achieved a mAP50 of 99.3%, outperforming the previous benchmark, the purpose-designed 'MangoYOLO', for the application of the real-time detection of mango fruit on images of tree canopies using an edge computing device as a viable use case. YOLOv8 and v9 models outperformed the benchmark MaskR-CNN model in terms of their accuracy and inference time, achieving up to a 98.8% mAP50 on fruit predictions and 66.2% on branches in a leafy canopy. For fruit sizing, the accuracy of YOLOv8m-seg was like that achieved using Mask R-CNN, but the inference time was much shorter, again an enabler for the field adoption of this technology. A branch avoidance algorithm was proposed, where the implementation of this algorithm in real-time on an edge computing device was enabled by the short inference time of a YOLOv8-seg model for branches and fruit. This capability contributes to the development of automated fruit harvesting.

Validation of In-House Imaging System via Code Verification on Petunia Images Collected at Increasing Fertilizer Rates and pHs.

In a production environment, delayed stress recognition can impact yield. Imaging can rapidly and effectively quantify stress symptoms using indexes such as normalized difference vegetation index (NDVI). Commercial systems are effective but cannot be easily customized for specific applications, particularly post-processing. We developed a low-cost customizable imaging system and validated the code to analyze images. Our objective was to verify the image analysis code and custom system could successfully quantify the changes in plant canopy reflectance. 'Supercascade Red', 'Wave© Purple', and 'Carpet Blue' Petunias (Petunia × hybridia) were transplanted individually and subjected to increasing fertilizer treatments and increasing substrate pH in a greenhouse. Treatments for the first trial were the addition of a controlled release fertilizer at six different rates (0, 0.5, 1, 2, 4, and 8 g/pot), and for the second trial, fertilizer solution with four pHs (4, 5.5, 7, and 8.5), with eight replications with one plant each. Plants were imaged twice a week using a commercial imaging system for fertilizer and thrice a week with the custom system for pH. The collected images were analyzed using an in-house program that calculated the indices for each pixel of the plant area. All cultivars showed a significant effect of fertilizer on the projected canopy size and dry weight of the above-substrate biomass and the fertilizer rate treatments (p < 0.01). Plant tissue nitrogen concentration as a function of the applied fertilizer rate showed a significant positive response for all three cultivars (p < 0.001). We verified that the image analysis code successfully quantified the changes in plant canopy reflectance as induced by increasing fertilizer application rate. There was no relationship between the pH and NDVI values for the cultivars tested (p > 0.05). Manganese and phosphorus had no significance with chlorophyll fluorescence for 'Carpet Blue' and 'Wave© Purple' (p > 0.05), though 'Supercascade Red' was found to have significance (p < 0.01). pH did not affect plant canopy size. Chlorophyll fluorescence pixel intensity against the projected canopy size had no significance except in 'Wave© Purple' (p = 0.005). NDVI as a function of the projected canopy size had no statistical significance. We verified the ability of the imaging system with integrated analysis to quantify nutrient deficiency-induced variability in plant canopies by increasing pH levels.

A Novel End-to-End Deep Learning Framework for Chip Packaging Defect Detection.

As semiconductor chip manufacturing technology advances, chip structures are becoming more complex, leading to an increased likelihood of void defects in the solder layer during packaging. However, identifying void defects in packaged chips remains a significant challenge due to the complex chip background, varying defect sizes and shapes, and blurred boundaries between voids and their surroundings. To address these challenges, we present a deep-learning-based framework for void defect segmentation in chip packaging. The framework consists of two main components: a solder region extraction method and a void defect segmentation network. The solder region extraction method includes a lightweight segmentation network and a rotation correction algorithm that eliminates background noise and accurately captures the solder region of the chip. The void defect segmentation network is designed for efficient and accurate defect segmentation. To cope with the variability of void defect shapes and sizes, we propose a Mamba model-based encoder that uses a visual state space module for multi-scale information extraction. In addition, we propose an interactive dual-stream decoder that uses a feature correlation cross gate module to fuse the streams' features to improve their correlation and produce more accurate void defect segmentation maps. The effectiveness of the framework is evaluated through quantitative and qualitative experiments on our custom X-ray chip dataset. Furthermore, the proposed void defect segmentation framework for chip packaging has been applied to a real factory inspection line, achieving an accuracy of 93.3% in chip qualification.

Improved localization and segmentation of spinal bone metastases in MRI with nnUNet radiomics.

Objective: Variability exists in the subjective delineation of tumor areas in MRI scans of patients with spinal bone metastases. This research aims to investigate the efficacy of the nnUNet radiomics model for automatic segmentation and identification of spinal bone metastases. Methods: A cohort of 118 patients diagnosed with spinal bone metastases at our institution between January 2020 and December 2023 was enrolled. They were randomly divided into a training set (n = 78) and a test set (n = 40). The nnUNet radiomics segmentation model was developed, employing manual delineations of tumor areas by physicians as the reference standard. Both methods were utilized to compute tumor area measurements, and the segmentation performance and consistency of the nnUNet model were assessed. Results: The nnUNet model demonstrated effective localization and segmentation of metastases, including smaller lesions. The Dice coefficients for the training and test sets were 0.926 and 0.824, respectively. Within the test set, the Dice coefficients for lumbar and thoracic vertebrae were 0.838 and 0.785, respectively. Strong linear correlation was observed between the nnUNet model segmentation and physician-delineated tumor areas in 40 patients (R 2 = 0.998, P < 0.001). Conclusions: The nnUNet model exhibits efficacy in automatically localizing and segmenting spinal bone metastases in MRI scans.

Machine Learning for Deconvolution and Segmentation of Hyperspectral Imaging Data from Biopharmaceutical Resins.

Biopharmaceutical resins are pivotal inert matrices used across industry and academia, playing crucial roles in a myriad of applications. For biopharmaceutical process research and development applications, a deep understanding of the physical and chemical properties of the resin itself is frequently required, including for drug purification, drug delivery, and immobilized biocatalysis. Nevertheless, the prevailing methodologies currently employed for elucidating these important aspects of biopharmaceutical resins are often lacking, frequently require significant sample alteration, are destructive or ionizing in nature, and may not adequately provide representative information. In this work, we propose the use of unsupervised machine learning technologies, in the form of both non-negative matrix factorization (NMF) and k-means segmentation, in conjugation with Raman hyperspectral imaging to rapidly elucidate the molecular and spatial properties of biopharmaceutical resins. Leveraging our proposed technology, we offer a new approach to comprehensively understanding important resin-based systems for application across biopharmaceuticals and beyond. Specifically, focusing herein on a representative resin widely utilized across the industry (i.e., Immobead 150P), our findings showcase the ability of our machine learning-based technology to molecularly identify and spatially resolve all chemical species present. Further, we offer a comprehensive evaluation of optimal excitation for hyperspectral imaging data collection, demonstrating results across 532, 638, and 785 nm excitation. In all cases, our proposed technology deconvoluted, both spatially and spectrally, resin and glass substrates via NMF. After NMF deconvolution, image segmentation was also successfully accomplished in all data sets via k-means clustering. To the best of our knowledge, this is the first report utilizing the combination of two unsupervised machine learning methodologies, combining NMF and k-means, for the rapid deconvolution and segmentation of biopharmaceutical resins. As such, we offer a powerful new data-rich experimentation tool for application across multidisciplinary fields for a deeper understanding of resins.

GU-Net: Causal relationship-based generative medical image segmentation model.

Due to significant anatomical variations in medical images across different cases, medical image segmentation is a highly challenging task. Convolutional neural networks have shown faster and more accurate performance in medical image segmentation. However, existing networks for medical image segmentation mostly rely on independent training of the model using data samples and loss functions, lacking interactive training and feedback mechanisms. This leads to a relatively singular training approach for the models, and furthermore, some networks can only perform segmentation for specific diseases. In this paper, we propose a causal relationship-based generative medical image segmentation model named GU-Net. We integrate a counterfactual attention mechanism combined with CBAM into the decoder of U-Net as a generative network, and then combine it with a GAN network where the discriminator is used for backpropagation. This enables alternate optimization and training between the generative network and discriminator, enhancing the expressive and learning capabilities of the network model to output prediction segmentation results closer to the ground truth. Additionally, the interaction and transmission of information help the network model capture richer feature representations, extract more accurate features, reduce overfitting, and improve model stability and robustness through feedback mechanisms. Experimental results demonstrate that our proposed GU-Net network achieves better segmentation performance not only in cases with abundant data samples and relatively simple segmentation targets or high contrast between the target and background regions but also in scenarios with limited data samples and challenging segmentation tasks. Comparing with existing U-Net networks with attention mechanisms, GU-Net consistently improves Dice scores by 1.19%, 2.93%, 5.01%, and 5.50% on ISIC 2016, ISIC 2017, ISIC 2018, and Gland Segmentation datasets, respectively.

HEDN: multi-oriented hierarchical extraction and dual-frequency decoupling network for 3D medical image segmentation.

Previous 3D encoder-decoder segmentation architectures struggled with fine-grained feature decomposition, resulting in unclear feature hierarchies when fused across layers. Furthermore, the blurred nature of contour boundaries in medical imaging limits the focus on high-frequency contour features. To address these challenges, we propose a Multi-oriented Hierarchical Extraction and Dual-frequency Decoupling Network (HEDN), which consists of three modules: Encoder-Decoder Module (E-DM), Multi-oriented Hierarchical Extraction Module (Multi-HEM), and Dual-frequency Decoupling Module (Dual-DM). The E-DM performs the basic encoding and decoding tasks, while Multi-HEM decomposes and fuses spatial and slice-level features in 3D, enriching the feature hierarchy by weighting them through 3D fusion. Dual-DM separates high-frequency features from the reconstructed network using self-supervision. Finally, the self-supervised high-frequency features separated by Dual-DM are inserted into the process following Multi-HEM, enhancing interactions and complementarities between contour features and hierarchical features, thereby mutually reinforcing both aspects. On the Synapse dataset, HEDN outperforms existing methods, boosting Dice Similarity Score (DSC) by 1.38% and decreasing 95% Hausdorff Distance (HD95) by 1.03 mm. Likewise, on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset, HEDN achieves  0.5% performance gains across all categories.

Complementary information mutual learning for multimodality medical image segmentation.

Radiologists must utilize medical images of multiple modalities for tumor segmentation and diagnosis due to the limitations of medical imaging technology and the diversity of tumor signals. This has led to the development of multimodal learning in medical image segmentation. However, the redundancy among modalities creates challenges for existing subtraction-based joint learning methods, such as misjudging the importance of modalities, ignoring specific modal information, and increasing cognitive load. These thorny issues ultimately decrease segmentation accuracy and increase the risk of overfitting. This paper presents the complementary information mutual learning (CIML) framework, which can mathematically model and address the negative impact of inter-modal redundant information. CIML adopts the idea of addition and removes inter-modal redundant information through inductive bias-driven task decomposition and message passing-based redundancy filtering. CIML first decomposes the multimodal segmentation task into multiple subtasks based on expert prior knowledge, minimizing the information dependence between modalities. Furthermore, CIML introduces a scheme in which each modality can extract information from other modalities additively through message passing. To achieve non-redundancy of extracted information, the redundant filtering is transformed into complementary information learning inspired by the variational information bottleneck. The complementary information learning procedure can be efficiently solved by variational inference and cross-modal spatial attention. Numerical results from the verification task and standard benchmarks indicate that CIML efficiently removes redundant information between modalities, outperforming SOTA methods regarding validation accuracy and segmentation effect. To emphasize, message-passing-based redundancy filtering allows neural network visualization techniques to visualize the knowledge relationship among different modalities, which reflects interpretability.

Children Are Not Small Adults: Addressing Limited Generalizability of an Adult Deep Learning CT Organ Segmentation Model to the Pediatric Population.

Deep learning (DL) tools developed on adult data sets may not generalize well to pediatric patients, posing potential safety risks. We evaluated the performance of TotalSegmentator, a state-of-the-art adult-trained CT organ segmentation model, on a subset of organs in a pediatric CT dataset and explored optimization strategies to improve pediatric segmentation performance. TotalSegmentator was retrospectively evaluated on abdominal CT scans from an external adult dataset (n = 300) and an external pediatric data set (n = 359). Generalizability was quantified by comparing Dice scores between adult and pediatric external data sets using Mann-Whitney U tests. Two DL optimization approaches were then evaluated: (1) 3D nnU-Net model trained on only pediatric data, and (2) an adult nnU-Net model fine-tuned on the pediatric cases. Our results show TotalSegmentator had significantly lower overall mean Dice scores on pediatric vs. adult CT scans (0.73 vs. 0.81, P < .001) demonstrating limited generalizability to pediatric CT scans. Stratified by organ, there was lower mean pediatric Dice score for four organs (P < .001, all): right and left adrenal glands (right adrenal, 0.41 [0.39-0.43] vs. 0.69 [0.66-0.71]; left adrenal, 0.35 [0.32-0.37] vs. 0.68 [0.65-0.71]); duodenum (0.47 [0.45-0.49] vs. 0.67 [0.64-0.69]); and pancreas (0.73 [0.72-0.74] vs. 0.79 [0.77-0.81]). Performance on pediatric CT scans improved by developing pediatric-specific models and fine-tuning an adult-trained model on pediatric images where both methods significantly improved segmentation accuracy over TotalSegmentator for all organs, especially for smaller anatomical structures (e.g., > 0.2 higher mean Dice for adrenal glands; P < .001).

LIT-Unet: a lightweight and effective model for medical image segmentation.

This study aimed to design a simple and efficient automatic segmentation model for medical images, so as to facilitate doctors to make more accurate diagnosis and treatment plan. A hybrid lightweight network LIT-Unet with symmetric encoder-decoder U-shaped architecture is proposed. Synapse multi-organ segmentation dataset and automated cardiac diagnosis challenge (ACDC) dataset were used to test the segmentation performance of the method. Two indexes, Dice similarity coefficient (DSC ↑) and 95% Hausdorff distance (HD95 ↓), were used to evaluate and compare the segmentation ability with the current advanced methods. Ablation experiments were conducted to demonstrate the lightweight nature and effectiveness of our model. For Synapse dataset, our model achieves a higher DSC score (80.40%), an improvement of 3.8% over the typical hybrid model (TransUnet). The 95 HD value is low at 20.67%. For ACDC dataset, LIT-Unet achieves the optimal average DSC (%) of 91.84 compared with other networks listed. Compared to patch expanding, the DSC of our model is intuitively improved by 1.62% with the help of deformable token merging (DTM). These results show that the proposed hierarchical LIT-Unet can achieve significant accuracy and is expected to provide a reliable basis for clinical diagnosis and treatment.

A modified U-Net to detect real sperms in videos of human sperm cell.

Background: This study delves into the crucial domain of sperm segmentation, a pivotal component of male infertility diagnosis. It explores the efficacy of diverse architectural configurations coupled with various encoders, leveraging frames from the VISEM dataset for evaluation. Methods: The pursuit of automated sperm segmentation led to the examination of multiple deep learning architectures, each paired with distinct encoders. Extensive experimentation was conducted on the VISEM dataset to assess their performance. Results: Our study evaluated various deep learning architectures with different encoders for sperm segmentation using the VISEM dataset. While each model configuration exhibited distinct strengths and weaknesses, UNet++ with ResNet34 emerged as a top-performing model, demonstrating exceptional accuracy in distinguishing sperm cells from non-sperm cells. However, challenges persist in accurately identifying closely adjacent sperm cells. These findings provide valuable insights for improving automated sperm segmentation in male infertility diagnosis. Discussion: The study underscores the significance of selecting appropriate model combinations based on specific diagnostic requirements. It also highlights the challenges related to distinguishing closely adjacent sperm cells. Conclusion: This research advances the field of automated sperm segmentation for male infertility diagnosis, showcasing the potential of deep learning techniques. Future work should aim to enhance accuracy in scenarios involving close proximity between sperm cells, ultimately improving clinical sperm analysis.

A flexible 2.5D medical image segmentation approach with in-slice and cross-slice attention.

Deep learning has become the de facto method for medical image segmentation, with 3D segmentation models excelling in capturing complex 3D structures and 2D models offering high computational efficiency. However, segmenting 2.5D images, characterized by high in-plane resolution but lower through-plane resolution, presents significant challenges. While applying 2D models to individual slices of a 2.5D image is feasible, it fails to capture the spatial relationships between slices. On the other hand, 3D models face challenges such as resolution inconsistencies in 2.5D images, along with computational complexity and susceptibility to overfitting when trained with limited data. In this context, 2.5D models, which capture inter-slice correlations using only 2D neural networks, emerge as a promising solution due to their reduced computational demand and simplicity in implementation. In this paper, we introduce CSA-Net, a flexible 2.5D segmentation model capable of processing 2.5D images with an arbitrary number of slices. CSA-Net features an innovative Cross-Slice Attention (CSA) module that effectively captures 3D spatial information by learning long-range dependencies between the center slice (for segmentation) and its neighboring slices. Moreover, CSA-Net utilizes the self-attention mechanism to learn correlations among pixels within the center slice. We evaluated CSA-Net on three 2.5D segmentation tasks: (1) multi-class brain MR image segmentation, (2) binary prostate MR image segmentation, and (3) multi-class prostate MR image segmentation. CSA-Net outperformed leading 2D, 2.5D, and 3D segmentation methods across all three tasks, achieving average Dice coefficients and HD95 values of 0.897 and 1.40 mm for the brain dataset, 0.921 and 1.06 mm for the prostate dataset, and 0.659 and 2.70 mm for the ProstateX dataset, demonstrating its efficacy and superiority. Our code is publicly available at: https://github.com/mirthAI/CSA-Net.

Automatic Segmentation of Ultrasound-Guided Quadratus Lumborum Blocks Based on Artificial Intelligence.

Ultrasound-guided quadratus lumborum block (QLB) technology has become a widely used perioperative analgesia method during abdominal and pelvic surgeries. Due to the anatomical complexity and individual variability of the quadratus lumborum muscle (QLM) on ultrasound images, nerve blocks heavily rely on anesthesiologist experience. Therefore, using artificial intelligence (AI) to identify different tissue regions in ultrasound images is crucial. In our study, we retrospectively collected 112 patients (3162 images) and developed a deep learning model named Q-VUM, which is a U-shaped network based on the Visual Geometry Group 16 (VGG16) network. Q-VUM precisely segments various tissues, including the QLM, the external oblique muscle, the internal oblique muscle, the transversus abdominis muscle (collectively referred to as the EIT), and the bones. Furthermore, we evaluated Q-VUM. Our model demonstrated robust performance, achieving mean intersection over union (mIoU), mean pixel accuracy, dice coefficient, and accuracy values of 0.734, 0.829, 0.841, and 0.944, respectively. The IoU, recall, precision, and dice coefficient achieved for the QLM were 0.711, 0.813, 0.850, and 0.831, respectively. Additionally, the Q-VUM predictions showed that 85% of the pixels in the blocked area fell within the actual blocked area. Finally, our model exhibited stronger segmentation performance than did the common deep learning segmentation networks (0.734 vs. 0.720 and 0.720, respectively). In summary, we proposed a model named Q-VUM that can accurately identify the anatomical structure of the quadratus lumborum in real time. This model aids anesthesiologists in precisely locating the nerve block site, thereby reducing potential complications and enhancing the effectiveness of nerve block procedures.

SeqSeg: Learning Local Segments for Automatic Vascular Model Construction.

Computational modeling of cardiovascular function has become a critical part of diagnosing, treating and understanding cardiovascular disease. Most strategies involve constructing anatomically accurate computer models of cardiovascular structures, which is a multistep, time-consuming process. To improve the model generation process, we herein present SeqSeg (sequential segmentation): a novel deep learning-based automatic tracing and segmentation algorithm for constructing image-based vascular models. SeqSeg leverages local U-Net-based inference to sequentially segment vascular structures from medical image volumes. We tested SeqSeg on CT and MR images of aortic and aortofemoral models and compared the predictions to those of benchmark 2D and 3D global nnU-Net models, which have previously shown excellent accuracy for medical image segmentation. We demonstrate that SeqSeg is able to segment more complete vasculature and is able to generalize to vascular structures not annotated in the training data.

Neural Memory State Space Models for Medical Image Segmentation.

With the rapid advancement of deep learning, computer-aided diagnosis and treatment have become crucial in medicine. UNet is a widely used architecture for medical image segmentation, and various methods for improving UNet have been extensively explored. One popular approach is incorporating transformers, though their quadratic computational complexity poses challenges. Recently, State-Space Models (SSMs), exemplified by Mamba, have gained significant attention as a promising alternative due to their linear computational complexity. Another approach, neural memory Ordinary Differential Equations (nmODEs), exhibits similar principles and achieves good results. In this paper, we explore the respective strengths and weaknesses of nmODEs and SSMs and propose a novel architecture, the nmSSM decoder, which combines the advantages of both approaches. This architecture possesses powerful nonlinear representation capabilities while retaining the ability to preserve input and process global information. We construct nmSSM-UNet using the nmSSM decoder and conduct comprehensive experiments on the PH2, ISIC2018, and BU-COCO datasets to validate its effectiveness in medical image segmentation. The results demonstrate the promising application value of nmSSM-UNet. Additionally, we conducted ablation experiments to verify the effectiveness of our proposed improvements on SSMs and nmODEs.

Characteristics of Open-Graded Friction Course Macrotexture and Macrostructure and Its Effect on Skid Resistance under Rainfall.

An Open-Graded Friction Course (OGFC) presents a rough surface and a porous structure and provides skid resistance under wet conditions, differing from that of a dense graded mixture. This study explored the distribution of surface macrotexture with depth in OGFC. Using cross-sectional images and semantic image segmentation techniques, the internal structure, porosity, and void size distribution were analyzed to assess the effectiveness of rainfall drainage. Skid resistance was evaluated with a British Pendulum Tester, focusing on the influence of surface macrotexture and internal macrostructure, particularly with regard to contact depth. Results show that finer gradations increase surface roughness peaks, which are concentrated near the top surface. In contrast, coarser mixtures exhibit a greater effective contact depth and more peaks with higher curvature. Finer gradations also result in lower porosity, greater void dispersion, and smaller average void diameters. During heavy rainfall, OGFC-13 exhibits the highest friction coefficient due to its effective contact, surface roughness, and internal voids, which facilitate water expulsion. This research provides insights into the skid resistance mechanism of OGFC in wet conditions and offers practical guidance for selecting the optimal gradation.