ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2018
Hematoma Segmentation Using Dilated Convolutional Neural Network.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?Medicine · 2022Pooled it
- Multifunctional Nanoparticles in Traumatic Brain Injury: From Targeted Imaging and Diagnosis to Innovative Therapeutics.International journal of nanomedicine · 2026Review
- Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation-A Review.Life (Basel, Switzerland) · 2025Review
- AI-Based Decision Support System for Traumatic Brain Injury: A Survey.Diagnostics (Basel, Switzerland) · 2023Review
- Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives.International journal of environmental research and public health · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Traumatic brain injury (TBI) is a global health challenge. Accurate and fast automatic detection of hematoma in the brain is essential for TBI diagnosis and treatment. In this study, we developed a fully automated system to detect and segment hematoma regions in head Computed Tomography (CT) images of patients with acute TBI. We adapted the structure of a fully convolutional network by introducing dilated convolution and removing down-sampling and up-sampling layers. Skip layers are also used to combine low-level features and high-level features. By integrating the information from different scales without losing spatial resolution, the network can perform more accurate segmentation. Our final hematoma segmentations achieved the Dice, sensitivity, and specificity of 0.62, 0.81, and 0.96, respectively, which outperformed the results from previous methods.
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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.