Trial reportSichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition2022
[Application of Improved Unet Network in the Recognition and Segmentation of Hemorrhage Regions in Brain CT Images].
Trial report in Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Application of improved Unet network in the recognition and segmentation of lung CT images in patients with pneumoconiosis.BMC medical imaging · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo examine the performance and application value of improved Unet network technology in the recognition and segmentation of hemorrhage regions in brain CT images.
methodsA total of 476 brain CT images of patients with spontaneous intracerebral hemorrhage (SICH) were retrospectively included. The improved Unet network was used to identify and segment the hemorrhage regions in the patients' brain CT images. The CT imaging data of the hemorrhage regions were manually labelled by clinicians. After randomized sorting, 430 data sets from 106 patients were selected for inclusion in the training set and 46 data sets from 11 patients were included in the test set. After data enhancement, the experimental data set underwent network training and model testing in order to assess the segmentation performance. The segmentation results were compared with the those of the Unet network (Base), FCN-8s network and Unet++ network.
resultsIn the segmentation of brain CT image hemorrhage region with the improved Unet network, the three evaluation indicators of Dice similarity coefficient, positive predictive value (PPV), and sensitivity coefficient (SC) reached 0.8738, 0.9011 and 0.8648, respectively, increasing by 8.80%, 7.14% and 8.96%, respectively, compared with those of FCN-8s, and increasing by 4.56%, 4.44% and 4.15%, respectively, compared with those of Unet network (Base). The improved Unet network also showed better segmentation performance than that of Unet++ network.
conclusionThe improved method based on Unet network proposed in this report displayed good performance in the recognition and segmentation of hemorrhage regions in brain CT images, and is an appropriate method for the recognition and segmentation of hemorrhage regions in brain CT images, showing potential application value for assisting clinical decision-making and preventing early hematoma expansion.
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.