ArticleJournal of healthcare engineering2018
Pixel-Label-Based Segmentation of Cross-Sectional Brain MRI Using Simplified SegNet Architecture-Based CNN.
Article in Journal of healthcare engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed.
- Variational Autoencoders-BasedSelf-Learning Model for Tumor Identification and Impact Analysis from 2-D MRI Images.Journal of healthcare engineering · 2023Article
- SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans.Sensors (Basel, Switzerland) · 2022Article
- Improving across-dataset brain tissue segmentation for MRI imaging using transformer.Frontiers in neuroimaging · 2022Article
- Deep learning based neural network application for automatic ultrasonic computed tomographic bone image segmentation.Multimedia tools and applications · 2022Article
- Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement.Sensors (Basel, Switzerland) · 2021Article
- Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves.Sensors (Basel, Switzerland) · 2021Article
- Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture.PloS one · 2020Article
- Appraisal of Deep-Learning Techniques on Computer-Aided Lung Cancer Diagnosis with Computed Tomography Screening.Journal of medical physicsArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Using deep neural networks for segmenting an MRI image of heterogeneously distributed pixels into a specific class assigning a label to each pixel is the concept of the proposed approach. This approach facilitates the application of the segmentation process on a preprocessed MRI image, with a trained network to be utilized for other test images. As labels are considered expensive assets in supervised training, fewer training images and training labels are used to obtain optimal accuracy. To validate the performance of the proposed approach, an experiment is conducted on other test images (available in the same database) that are not part of the training; the obtained result is of good visual quality in terms of segmentation and quite similar to the ground truth image. The average computed Dice similarity index for the test images is approximately 0.8, whereas the Jaccard similarity measure is approximately 0.6, which is better compared to other methods. This implies that the proposed method can be used to obtain reference images almost similar to the segmented ground truth images.
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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.