Segmentation Method Research Articles

A statistical-based automatic detection of a low-contrast object in the ACR CT phantom for measuring contrast-to-noise ratio of CT images.


This study aimed to develop a new method for automated contrast-to-noise ratio (CNR) measurement using the low-contrast object in the ACR CT phantom.
Methods:
The ACR CT 464 phantom was scanned by 25 CT scanners installed at 25 different hospitals. AROI was placed in a specific radial location and was then rotated around 3600 increments of 20. At each position, the average CT number within the ROI was calculated. After one complete rotation, a profile of the average CT number around the 3600 degree was obtained. The center coordinate of the low-contrast object was determined from the maximum value of the profile. The CNR was calculated based on the average CT number and noise within the ROI in the low-contrast object and the ROI in the background, i.e., at the center of the phantom. The results were compared to a previous method based on a segmentation approach.
Results:
From 325 image samples of the 25 CT scanners, the proposed method successfully (100%) located the ROI within the low-contrast objects of all images used. The success rate of the segmentation method was only 56%.
Conclusion:
A new method for measuring CNR in the ACR CT phantom has been proposed and implemented. It is more powerful than a previous method based on segmentation.&#xD.

WGAN-based multi-structure segmentation of vertebral cross-section MRI using ResU-Net and clustered transformer

The vertebral foramen, lamina, and vertebral body are three critical components of the spine structure, essential for maintaining spinal connectivity and stability. Accurately segmenting lumbar structures such as the vertebral body, vertebral foramen, and lamina in MRI cross-sections helps doctors better understand and diagnose the pathological causes of spine-related diseases. This study presents a multi-structure semantic segmentation method for vertebral transverse section MRI slices using WGAN with a residual U-Net and clustered Transformer. The generator network was replaced with a combination of a residual U-Net and a clustered Transformer-based segmentation network. The enhanced U-Net encoder, utilizing dilated convolutions and residual structures, improved multi-scale feature extraction capabilities. Meanwhile, the clustered Transformer structure, with reduced progressive linear complexity, ensured the extraction of global positional information. The results of multiple experiments show that the Dice coefficient for vertebral body segmentation increased by 3.1%, the Hausdorff distance decreased by 0.6 mm, mIOU improved by 4.1–96.2%, and PPV increased by 2.0–98.8% compared to mainstream segmentation models. These improvements are statistically significant (p < 0.05).Ablation experiments further validated the effectiveness of the proposed enhanced modules in improving segmentation accuracy for the three target structures.

Glioma subtype prediction based on radiomics of tumor and peritumoral edema under automatic segmentation

Comprehensive and non-invasive preoperative molecular diagnosis is important for prognostic and therapy decision-making in adult-type diffuse gliomas. We employed a deep learning method for automatic segmentation of brain gliomas directly from conventional magnetic resonance imaging (MRI) scans of the tumor core and peritumoral edema regions based on available glioma MRI data provided in the BraTS2021. Three-dimensional volumes of interest were segmented from 424 cases of glioma imaging data retrospectively obtained from two medical centers using the segmentation method and radiomic features were extracted. We developed a subtype prediction model based on extracted radiomic features and analyzed significance and correlations between glioma morphological characteristics and pathological features using data from patients with adult-type diffuse glioma. The automated segmentation achieved mean Dice scores of 0.884 and 0.889 for the tumor core and whole tumor, respectively. The area under the receiver operating characteristic curve for the prediction of adult-type diffuse gliomas subtypes was 0.945. “Glioblastoma, IDH-wildtype”, “Astrocytoma, IDH-mutant”, and “Oligodendroglioma, IDH-mutant, 1p/19q-coded” showed AUCs of 0.96, 0.914, and 0.961, respectively, for subtype prediction. Glioma morphological characteristics, molecular and pathological levels, and clinical data showed significant differences and correlations. An automatic segmentation model for gliomas based on 3D U-Nets was developed, and the prediction model for gliomas built using the parameters obtained from the automatic segmentation model showed high overall performance.

SYMPATHIQUE: image-based tracking of symptoms and monitoring of pathogenesis to decompose quantitative disease resistance in the field

BackgroundQuantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions.ResultsWe present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, spatial alignment of images, symptom tracking, and leaf- and symptom characterization. The average accuracy of the spatial alignment of images in a time series was approximately 5 pixels (~ 0.15 mm). Leaf-level symptom counts as well as individual symptom property measurements revealed stable patterns over time that were generally in excellent agreement with visual impressions. This provided strong evidence for the robustness of the methodology to variability typically inherent in field data. Contrasting patterns in the number of lesions resulting from separate infection events and lesion expansion dynamics were observed across wheat genotypes. The number of separate infection events and average lesion size contributed to different degrees to overall disease intensity, possibly indicating distinct and complementary mechanisms of QR.ConclusionsThe proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models. Details of pathogenesis can translate into specific symptom phenotypes resolvable using time series of high-resolution RGB images, which may improve biological understanding of plant-pathogen interactions as well as interactions in disease complexes.

Revolutionizing early breast cancer screening: Advanced multi-spectral transmission imaging classification with improved Otsu's method and K-means clustering

Breast cancer stands as a formidable danger to the health of women worldwide, underscoring the critical need for effective screening methods. Multispectral transmission imaging offers a promising avenue due to its non-invasive potential for early screening. Some researchers already suggested registration to solve the problem of jitters due to respiration and movement and frame accumulation technology to solve the low grayscale problem. However, the classification of blood vessels and breast tissue in breast images often suffers from low signal-to-noise ratio (SNR) and low contrast, hindering accurate classification. This paper proposes a novel improved Otsu's method with K-Means clustering to address this challenge. The proposed method aims to enhance the foundation for classification using multispectral transmission images. The study utilizes multispectral transmission images captured at four wavelengths, representing an innovative avenue for early, affordable breast cancer screening research. Initially, 300 images are registered and accumulated to prove the efficiency of the suggested methodology. Then, median filtering is applied to reduce noise in the images. Improved Otsu's segmentation method is then employed to separate blood vessels from breast tissue. After that, K-means clustering is utilized to accurately classify these components. The results of the proposed method demonstrate significant improvements in classification accuracy and grayscale contrast of multispectral breast images. By effectively distinguishing blood vessels and breast tissue, the proposed methodology addresses the inherent challenges of low contrast in multispectral transmission imaging. This advancement offers a clearer pathway for early breast cancer screening.

Automatic segmentation and visualization of cortical and marrow bone in mandibular condyle on CBCT: a preliminary exploration of clinical application.

To develop a deep learning-based automatic segmentation method for cortex and marrow in mandibular condyle on cone-beam computed tomography (CBCT) images and explore its clinical application. 825 condyles of 490 CBCT images from 3 centers of Stomatology hospitalaffliated to Zhejiang University School of Medicinewere collected. A deep learning model was developed for simultaneous segmentation of cortex and marrow in mandibular condyle. It included a region of interest extraction network and a segmentation network based on 3D U-net, with modifications made to improve the segmentation boundaries. To evaluate its clinical potential, the model's segmentation efficiency and accuracy were compared with those of both junior and senior oral and maxillofacial radiologists. Additionally, the model's ability to assist junior radiologists in diagnosis through visualization and quantitative analysis of the generated 3D model was also assessed. The Dice similarity coefficient of the deep learning model was 0.901 (cortex), 0.969 (marrow), and 0.982 (entire condyle). Hausdorff distance was 0.755mm (cortex), 0.826mm (marrow), and 0.760mm (entire condyle). The model outperformed radiologists across all segmentation metrics, completing the task in merely 15.06s. With the assistance of visualization and quantitative analysis generated from the model's segmentation, the diagnostic accuracy of junior radiologists significantly improved. The proposed deep learning-based model achieved accurate and efficient segmentation for mandibular condylar cortex and marrow. It possessed capability to generate precise 3D models, facilitating visual quantitative measurement and aiding in the diagnosis of condylar bony changes. This model holds potential for clinical applications in orthognathic surgery, orthodontic treatment, and other TMJ-related interventions.

Perirenal Fat CT Radiomics-Based Survival Model for Upper Tract Urothelial Carcinoma: Integrating Texture Features with Clinical Predictors

Background: Upper tract urothelial carcinoma (UTUC) presents significant challenges in prognostication due to its rarity and complex anatomy. This study introduces a novel approach integrating perirenal fat (PRF) radiomics with clinical factors to enhance prognostic accuracy in UTUC. Methods: The study retrospectively analyzed 103 UTUC patients who underwent radical nephroureterectomy. PRF radiomics features were extracted from preoperative CT scans using a semi-automated segmentation method. Three prognostic models were developed: clinical, radiomics, and combined. Model performance was assessed using concordance index (C-index), time-dependent Area Under the Curve (AUC), and integrated Brier score. Results: The combined model demonstrated superior performance (C-index: 0.784, 95% CI: 0.707–0.861) compared to the radiomics (0.759, 95% CI: 0.678–0.840) and clinical (0.653, 95% CI: 0.547–0.759) models. Time-dependent AUC analysis revealed the radiomics model’s particular strength in short-term prognosis (12-month AUC: 0.9281), while the combined model excelled in long-term predictions (60-month AUC: 0.8403). Key PRF radiomics features showed stronger prognostic value than traditional clinical factors. Conclusions: Integration of PRF radiomics with clinical data significantly improves prognostic accuracy in UTUC. This approach offers a more nuanced analysis of the tumor microenvironment, potentially capturing early signs of tumor invasion not visible through conventional imaging. The semi-automated PRF segmentation method presents advantages in reproducibility and ease of use, facilitating potential clinical implementation.

Analysis, Design, and Optimization of Segment‐Compensated PCB Coil for Multirelay Wireless Power Transfer System

ABSTRACTThis paper proposed a segment‐compensated printed circuit board (PCB) coil for multirelay wireless power transfer (WPT) system. The PCB coil is designed with variable width to improve the system transfer efficiency, and the segmented compensation method is applied to avoid the high voltage stress on resonant capacitors in case of the multirelay WPT system with a low load resistance operating at a constant voltage frequency. The segmented compensation capacitors are integrated into the PCB coil, so as to constitute the compact coupler used in multirelay WPT system. The optimization procedure of the segment‐compensated PCB coil is also provided. The quality factor of the PCB coil can be improved by optimizing the proportional coefficients of width variation. The voltage of capacitors can be reduced by optimizing the number of segments. A physical variable‐width PCB coil with 3 segments is constructed. The experimental prototypes of the three‐coil, four‐coil, and five‐coil WPT systems are built. The experimental results show that the voltage of the segmented compensation capacitors on each coil is almost uniformly distributed and does not exceed 250 V when the input voltage is 50 V and the load resistance is 10 Ω. Compared with the multirelay WPT system using the equal‐width PCB coil, the multirelay WPT system using the PCB coil designed in this paper has higher output power and higher efficiency.

COVID-19 CT Image Segmentation Based on Modified U-Net

Abstract. This paper presents a COVID-19 CT image segmentation method based on a modified U-Net model. In the study and treatment of COVID-19, the segmentation of CT images is crucial for understanding the virus's impact on lung tissues. Traditional image segmentation methods have limitations when dealing with the complex lung lesions caused by COVID-19. Therefore, we optimized the U-Net model's structure and introduced an efficient loss function along with image enhancement techniques to improve segmentation accuracy and computational efficiency. Experiments were conducted on the Medseg and Radiopaedia datasets. The results demonstrate that the modified U-Net model outperforms traditional methods in terms of segmentation accuracy and computational efficiency. Additionally, we discuss the advantages and limitations of the model and propose directions for future research.

A Novel Defect Segmentation Model via Integrating Enhanced U-Net with Convolutional Block Attention Mechanisms

Abstract. Defect segmentation in industrial quality control is pivotal for maintaining high product surface standards, but it remains a challenge due to complex defect patterns and varying backgrounds. Traditional segmentation methods often struggle with sensitivity and specificity, leading to either missed defects or false positives. In order to address the above issues this paper presents an advanced defect segmentation model that integrates a Convolutional Block Attention Module (CBAM) with an enhanced U-Net architecture. Our method leverages the spatial and channel-wise attention mechanisms to focus more accurately on subtle defects and remove background interference, enhancing the discriminative power of the feature maps generated by the U-Net. Experimental results on SD-saliency-900 industrial datasets demonstrate significant improvements in segmentation accuracy and robustness compared to existing state-of-the-art methods, confirming the effectiveness of integrating attention mechanisms in deep learning models for complex defect segmentation tasks. Specifically, our model achieved a mean Intersection over Union (mIoU) of 0.87, which is an increase of 5% over the traditional U-Net model and a 4% improvement compared to the DeepLabv3+ model. These results indicate a substantial enhancement in defect detection performance, validating the superiority of our approach.

LightUrban: Similarity Based Fine‐grained Instancing for Lightweighting Complex Urban Point Clouds

AbstractLarge‐scale urban point clouds play a vital role in various applications, while rendering and transmitting such data remains challenging due to its large volume, complicated structures, and significant redundancy. In this paper, we present LightUrban, the first point cloud instancing framework for efficient rendering and transmission of fine‐grained complex urban scenes. We first introduce a segmentation method to organize the point clouds into individual buildings and vegetation instances from coarse to fine. Next, we propose an unsupervised similarity detection approach to accurately group instances with similar shapes. Furthermore, a fast pose and size estimation component is applied to calculate the transformations between the representative instance and the corresponding similar instances in each group. By replacing individual instances with their group's representative instances, the data volume and redundancy can be dramatically reduced. Experimental results on large‐scale urban scenes demonstrate the effectiveness of our algorithm. To sum up, our method not only structures the urban point clouds but also significantly reduces data volume and redundancy, filling the gap in lightweighting urban landscapes through instancing.

Distilling Knowledge in Machine Translation of Agglutinative Languages with Backward and Morphological Decoders

Agglutinative languages often have morphologically complex words(MCWs) composed of multiple morphemes arranged in a hierarchical structure, posing significant challenges in translation tasks. We present a novel Knowledge Distillation approach tailored for improving the translation of such languages. Our method involves an encoder, a forward decoder, and two auxiliary decoders: a backward decoder and a morphological decoder. The forward decoder generates target morphemes autoregressively and is augmented by distilling knowledge from the auxiliary decoders. The backward decoder incorporates future context, while the morphological decoder integrates target-side morphological information. We have also designed a reliability estimation method to selectively distill only the reliable knowledge from these auxiliary decoders. Our approach relies on morphological word segmentation. We show that the word segmentation method based on unsupervised morphology learning outperforms the commonly used Byte Pair Encoding method on highly agglutinative languages in translation tasks. Our experiments conducted on English-Tamil, English-Manipuri, and English-Marathi datasets show that our proposed approach achieves significant improvements over strong Transformer-based NMT baselines.

GPF-Plus: a three-stage MLS point cloud ground segmentation method based on ground plane fitting

GPF-Plus: a three-stage MLS point cloud ground segmentation method based on ground plane fitting

Automatic delineation of cervical cancer target volumes in small samples based on multi-decoder and semi-supervised learning and clinical application

Radiotherapy has been demonstrated to be one of the most significant treatments for cervical cancer, during which accurate and efficient delineation of target volumes is critical. To alleviate the data demand of deep learning and promote the establishment and promotion of auto-segmentation models in small and medium-sized oncology departments and single centres, we proposed an auto-segmentation algorithm to determine the cervical cancer target volume in small samples based on multi-decoder and semi-supervised learning (MDSSL), and we evaluated the accuracy via an independent test cohort. In this study, we retrospectively collected computed tomography (CT) datasets from 71 pelvic cervical cancer patients, and a 3:4 ratio was used for the training and testing sets. The clinical target volumes (CTVs) of the primary tumour area (CTV1) and pelvic lymph drainage area (CTV2) were delineated. For definitive radiotherapy (dRT), the primary gross target volume (GTVp) was simultaneously delineated. According to the data characteristics for small samples, the MDSSL network structure based on 3D U-Net was established to train the model by combining clinical anatomical information, which was compared with other segmentation methods, including supervised learning (SL) and transfer learning (TL). The dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average surface distance (ASD) were used to evaluate the segmentation performance. The ability of the segmentation algorithm to improve the efficiency of online adaptive radiation therapy (ART) was assessed via geometric indicators and a subjective evaluation of radiation oncologists (ROs) in prospective clinical applications. Compared with the SL model and TL model, the proposed MDSSL model displayed the best DSC, HD95 and ASD overall, especially for the GTVp of dRT. We calculated the above geometric indicators in the range of the ground truth (head-foot direction). In the test set, the DSC, HD95 and ASD of the MDSSL model were 0.80/5.85 mm/0.95 mm for CTV1 of post-operative radiotherapy (pRT), 0.84/ 4.88 mm/0.73 mm for CTV2 of pRT, 0.84/6.58 mm/0.89 mm for GTVp of dRT, 0.85/5.36 mm/1.35 mm for CTV1 of dRT, and 0.84/4.09 mm/0.73 mm for CTV2 of dRT, respectively. In a prospective clinical study of online ART, the target volume modification time (MTime) was 3–5 min for dRT and 2–4 min for pRT, and the main duration of CTV1 modification was approximately 2 min. The introduction of the MDSSL method successfully improved the accuracy of auto-segmentation for the cervical cancer target volume in small samples, showed good consistency with RO delineation and satisfied clinical requirements. In this prospective online ART study, the application of the segmentation model was demonstrated to be useful for reducing the target volume delineation time and improving the efficiency of the online ART workflow, which can contribute to the development and promotion of cervical cancer online ART.

A new strategy based on multi-source remote sensing data for improving the accuracy of land use/cover change classification.

Land Use/Cover Change (LUCC) plays a crucial role in sustainable land management and regional planning. However, contemporary feature extraction approaches often prove inefficient at capturing critical data features, thereby complicating land cover categorization. In this research, we introduce a new feature extraction algorithm alongside a Segmented and Stratified Principal Component Analysis (SS-PCA) dimensionality reduction method based on correlation grouping. These methods are applied to UAV LiDAR and UAV HSI data collected from land use types (e.g., residential areas, agricultural lands) and specific species (e.g., tree species) in urban, agricultural, and natural environments to reflect the diversity of the study area and to demonstrate the ability of our methods to be applied in different classification scenarios. We utilize LiDAR and HSI data to extract 157 features, including intensity, height, Normalized Digital Surface Model (nDSM), spectral, texture, and index features, to identify the optimal feature subset. Subsequently, the best feature subset is inputted into a random forest classifier to classify the features. Our findings demonstrate that the SS-PCA method successfully enhances downscaled feature bands, reduces hyperspectral data noise, and improves classification accuracy (Overall Accuracy = 91.17%). Additionally, the CFW method effectively screens appropriate features, thereby increasing classification accuracy for LiDAR (Overall Accuracy = 78.10%), HIS (Overall Accuracy = 89.87%), and LiDAR + HIS (Overall Accuracy = 97.17%) data across various areas. Moreover, the integration of LiDAR and HSI data holds promise for significantly improving ground fine classification accuracy while mitigating issues such as the 'salt and pepper noise'. Furthermore, among individual features, the LiDAR intensity feature emerges as critical for enhancing classification accuracy, while among single-class features, the HSI feature proves most influential in improving classification accuracy.

Instance Segmentation XXL-CT Challenge of a Historic Airplane

Instance segmentation of compound objects in XXL-CT imagery poses a unique challenge in non-destructive testing. This complexity arises from the lack of known reference segmentation labels, limited applicable segmentation tools, as well as partially degraded image quality. To asses recent advancements in the field of machine learning-based image segmentation, the ‘Instance Segmentation XXL-CT Challenge of a Historic Airplane’ was conducted. The challenge aimed to explore automatic or interactive instance segmentation methods for an efficient delineation of the different aircraft components, such as screws, rivets, metal sheets or pressure tubes. We report the organization and outcome of this challenge and describe the capabilities and limitations of the submitted segmentation methods.

A Method for Retina Segmentation by Means of U-Net Network

Retinal image segmentation plays a critical role in diagnosing and monitoring ophthalmic diseases such as diabetic retinopathy and age-related macular degeneration. We propose a deep learning-based approach utilizing the U-Net network for the accurate and efficient segmentation of retinal images. U-Net, a convolutional neural network widely used for its performance in medical image segmentation, is employed to segment key retinal structures, including the optic disc and blood vessels. We evaluate the proposed model on a publicly available retinal image dataset, demonstrating interesting performance in automatic retina segmentation, thus showing the effectiveness of the proposed method. Our proposal provides a promising method for automated retinal image analysis, aiding in early disease detection and personalized treatment planning.

A method for automatic segmentation of computed tomograms with contrast

A method for automatic segmentation of computed tomograms with contrast

Investigation and Simulation Study on the Impact of Vegetation Cover Evolution on Watershed Soil Erosion

Soil erosion has always been a critical issue confronting watershed environments, impacting the progress of sustainable development. As an increasing number of countries turn their attention to this problem, numerous policies have been enacted to halt the progression of soil erosion. However, policy-driven interventions often lead to significant changes in watershed vegetation coverage, under which circumstances, the original sediment erosion models may fall short in terms of simulation accuracy. Taking the Kuye River watershed as the research subject, this study investigates soil erosion data spanning from 1981 to 2015 and utilizes the Revised Universal Soil Loss Equation (RUSLE) model to simulate soil erosion. It is found that the extensive planting of vegetation after 2000 has led to a rapid reduction in soil erosion within the Kuye River watershed. The original vegetation cover and management factor (C) proves inadequate in predicting the abrupt changes in vegetation coverage. Consequently, this study adopts two improved plant cover and management factor equations. We propose two new methods for calculating the vegetation cover and management factor, one using machine learning techniques and the other employing a segmented calculation approach. The machine learning approach utilizes the Eureqa software(version11.0，Cornell University，New York，American) to search for the relationship between Normalized Difference Vegetation Index (NDVI) and C, ultimately establishing an equation that describes this relationship. On the other hand, the piecewise method determines critical values based on data trends and provides separate formulas for C above and below these critical values. Both methods have achieved superior calculation accuracy. Specifically, the overall data calculation using the machine learning method achieved an determined coefficient (R2) of 0.5959, while the segmented calculation method achieved an R2 of 0.6649. Compared to the R2 calculated by the traditional RULSE method, these two new methods can more accurately predict soil erosion. The findings of this study can provide valuable theoretical reference for water and soil prediction in watersheds.

Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography

Corneal transplantation is the primary treatment for irreversible corneal diseases, but due to limited donor availability, bioengineered corneal equivalents are being developed as a solution, with biocompatibility, structural integrity, and physical function considered key factors. Since conventional evaluation methods may not fully capture the complex properties of the cornea, there is a need for advanced imaging and assessment techniques. In this study, we proposed a deep learning-based automatic segmentation method for transplanted bioengineered corneal equivalents using optical coherence tomography to achieve a highly accurate evaluation of graft integrity and biocompatibility. Our method provides quantitative individual thickness values, detailed maps, and volume measurements of the bioengineered corneal equivalents, and has been validated through 14 days of monitoring. Based on the results, it is expected to have high clinical utility as a quantitative assessment method for human keratoplasties, including automatic opacity area segmentation and implanted graft part extraction, beyond animal studies.