ArticleIEEE transactions on bio-medical engineering2018
Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans.
Article in IEEE transactions on bio-medical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Preoperative risk and postoperative outcome from subdural fluid collections in African infants with postinfectious hydrocephalus.Journal of neurosurgery. Pediatrics · 2022Trial
- Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus.NeuroImage. Clinical · 2021Trial
- Assessing CT-based volumetric analysis via deep learning for idiopathic normal pressure hydrocephalus.Brain communications · 2026Article
- DECE-Net: a dual-path encoder network with contour enhancement for pneumonia lesion segmentation.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Visual Prompting Based Incremental Learning for Semantic Segmentation of Multiplex Immuno-Flourescence Microscopy Imagery.Neuroinformatics · 2024Article
- Visual Prompting based Incremental Learning for Semantic Segmentation of Multiplex Immuno-Flourescence Microscopy Imagery.Research square · 2023Article
- Brain imaging with portable low-field MRI.Nature reviews bioengineering · 2023Article
- Deep learning in precision medicine and focus on glioma.Bioengineering & translational medicine · 2023Review
- BirCat Optimization for Automatic Segmentation of Brain Tumors and Pixel Change Detection Using Post-operative MRI Images.Journal of digital imaging · 2023Article
- GL-Segnet: Global-Local representation learning net for medical image segmentation.Frontiers in neuroscience · 2023Article
- The current and future roles of artificial intelligence in pediatric radiology.Pediatric radiology · 2022Review
- AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus.Neural computing & applications · 2022Article
- Fully Automatic Knee Bone Detection and Segmentation on Three-Dimensional MRI.Diagnostics (Basel, Switzerland) · 2022Article
- Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond.An international journal on information fusion · 2022Article
- Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure.Bioengineering (Basel, Switzerland) · 2021Article
- Machine learning predicts risk of cerebrospinal fluid shunt failure in children: a study from the hydrocephalus clinical research network.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2021Article
- Application of Evans Index in Normal Pressure Hydrocephalus Patients: A Mini Review.Frontiers in aging neuroscience · 2021Review
- Artificial intelligence in paediatric radiology: Future opportunities.The British journal of radiology · 2021Review
- Artificial intelligence for brain diseases: A systematic review.APL bioengineering · 2020Review
- A Method to Estimate Brain Volume from Head CT Images and Application to Detect Brain Atrophy in Alzheimer Disease.AJNR. American journal of neuroradiology · 2020Article
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Authors and funding
6 authors.
Funding
Abstract
objectiveHydrocephalus is a medical condition in which there is an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain imagery into brain tissue and CSF [before and after surgery, i.e., preoperative (pre-op) versus postoperative (post-op)] plays a crucial role in evaluating surgical treatment. Segmentation of pre-op images is often a relatively straightforward problem and has been well researched. However, segmenting post-op computational tomographic (CT) scans becomes more challenging due to distorted anatomy and subdural hematoma collections pressing on the brain. Most intensity- and feature-based segmentation methods fail to separate subdurals from brain and CSF as subdural geometry varies greatly across different patients and their intensity varies with time. We combat this problem by a learning approach that treats segmentation as supervised classification at the pixel level, i.e., a training set of CT scans with labeled pixel identities is employed.
methodsOur contributions include: 1) a dictionary learning framework that learns class (segment) specific dictionaries that can efficiently represent test samples from the same class while poorly represent corresponding samples from other classes; 2) quantification of associated computation and memory footprint; and 3) a customized training and test procedure for segmenting post-op hydrocephalic CT images.
resultsExperiments performed on infant CT brain images acquired from the CURE Children's Hospital of Uganda reveal the success of our method against the state-of-the-art alternatives. We also demonstrate that the proposed algorithm is computationally less burdensome and exhibits a graceful degradation against a number of training samples, enhancing its deployment potential.
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