ArticleJournal of imaging informatics in medicine2024
An Automated Multi-scale Feature Fusion Network for Spine Fracture Segmentation Using Computed Tomography Images.
Article in Journal of imaging informatics in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Towards Automated Spine Fracture Detection on Whole-Body CT of Polytraumatized Patients.Journal of imaging · 2026Article
- MRI-based deep learning radiomics model for automated classification of disc degeneration in the lumbar spine.Scientific reports · 2026Article
- Multi-scale generative adversarial network: three-dimensional reconstruction of the scoliotic spine from biplanar X-rays.Quantitative imaging in medicine and surgery · 2026Article
- Automated deep learning pipeline for measuring lumbar thecal sac AP diameter on mid-sagittal MR images.BMC medical imaging · 2026Article
- Review
- Smartphone-Based Oral Lesion Image Segmentation Using Deep Learning.Journal of imaging informatics in medicine · 2025Article
- Automated lumbar spine segmentation in MRI using an enhanced U-Net with inception module and dual-output mechanism.Scientific reports · 2025Article
- An automated hip fracture detection, classification system on pelvic radiographs and comparison with 35 clinicians.Scientific reports · 2025Article
- The use of a convolutional neural network to automate radiologic scoring of computed tomography of paranasal sinuses.Biomedical engineering online · 2025Article
- Deep learning based dual stage model for accurate nasogastric tube positioning in chest radiographs.Scientific reports · 2025Article
- Vision transformer and deep learning based weighted ensemble model for automated spine fracture type identification with GAN generated CT images.Scientific reports · 2025Article
- Attention LinkNet-152: a novel encoder-decoder based deep learning network for automated spine segmentation.Scientific reports · 2025Article
- Automatic detection, classification, and segmentation of sagittal MR images for diagnosing prolapsed lumbar intervertebral disc.Scientific reports · 2025Article
- The evolution of cervical spine trauma classification: a paradigm shift from morphological description to clinical decision-making.Frontiers in neurology · 2025Review
- Augmented prediction of vertebral collapse after osteoporotic vertebral compression fractures through parameter-efficient fine-tuning of biomedical foundation models.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spine fractures represent a critical health concern with far-reaching implications for patient care and clinical decision-making. Accurate segmentation of spine fractures from medical images is a crucial task due to its location, shape, type, and severity. Addressing these challenges often requires the use of advanced machine learning and deep learning techniques. In this research, a novel multi-scale feature fusion deep learning model is proposed for the automated spine fracture segmentation using Computed Tomography (CT) to these challenges. The proposed model consists of six modules; Feature Fusion Module (FFM), Squeeze and Excitation (SEM), Atrous Spatial Pyramid Pooling (ASPP), Residual Convolution Block Attention Module (RCBAM), Residual Border Refinement Attention Block (RBRAB), and Local Position Residual Attention Block (LPRAB). These modules are used to apply multi-scale feature fusion, spatial feature extraction, channel-wise feature improvement, segmentation border results border refinement, and positional focus on the region of interest. After that, a decoder network is used to predict the fractured spine. The experimental results show that the proposed approach achieves better accuracy results in solving the above challenges and also performs well compared to the existing segmentation methods.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.