ArticleJournal of healthcare engineering2021
Spine Medical Image Segmentation Based on Deep Learning.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 9 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
9 citing papers in PubMed.
- Attention LinkNet-152: a novel encoder-decoder based deep learning network for automated spine segmentation.Scientific reports · 2025Article
- Artificial intelligence-assisted magnetic resonance imaging technology in the differential diagnosis and prognosis prediction of endometrial cancer.Scientific reports · 2024Article
- An Automated Multi-scale Feature Fusion Network for Spine Fracture Segmentation Using Computed Tomography Images.Journal of imaging informatics in medicine · 2024Article
- Using Augmented Reality Technology to Optimize Transfacet Lumbar Interbody Fusion: A Case Report.Journal of clinical medicine · 2024Article
- An Automated Deep Learning Approach for Spine Segmentation and Vertebrae Recognition Using Computed Tomography Images.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies.Bioengineering (Basel, Switzerland) · 2023Review
- Retracted: Spine Medical Image Segmentation Based on Deep Learning.Journal of healthcare engineering · 2023Article
- Deep Learning Algorithms in the Automatic Segmentation of Liver Lesions in Ultrasound Investigations.Life (Basel, Switzerland) · 2022Article
- Neuronal Apoptosis in Patients with Liver Cirrhosis and Neuronal Epileptiform Discharge Model Based upon Multi-Modal Fusion Deep Learning.Journal of healthcare engineering · 2022Article
Corrections and comments
- Retraction · 2023-11-01Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim was to further explore the clinical value of deep learning algorithm in the field of spinal medical image segmentation, and this study designed an improved U-shaped network (BN-U-Net) algorithm and applied it to the spinal MRI medical image segmentation of 22 research objects. The application value of this algorithm in MRI image processing was comprehensively evaluated by accuracy (Acc), sensitivity (Sen), specificity (Spe), and area under curve (AUC). The results show that the image processing time of fully convolutional network (FCN) algorithm and U-Net algorithm is greater than 6 min, while the processing time of BN-U-Net algorithm is only 5-10 s, and the processing time is significantly shortened (
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.