AIMC Topic: Brain Stem
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A Cascaded Deep Convolutional Neural Network for Joint Segmentation and Genotype Prediction of Brainstem Gliomas.
IEEE transactions on bio-medical engineering
Jun 8, 2018
GOAL: Automatic segmentation of brainstem gliomas and prediction of genotype (H3 K27M) mutation status based on magnetic resonance (MR) images are crucial but challenging tasks for computer-aided diagnosis in neurosurgery. In this paper, we present a...
Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.
PloS one
May 23, 2016
Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate w...
Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: A clinical study.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
May 13, 2016
Delineation of organs at risk (OARs) is a crucial step in surgical and treatment planning in brain cancer, where precise OARs volume delineation is required. However, this task is still often manually performed, which is time-consuming and prone to o...
Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context.
International journal of computer assisted radiology and surgery
Jul 24, 2015
PURPOSE: To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk is required. This task is still manually performed, which is time-consuming and prone to observer variability. To address...
A multiregional multimodal machine learning model for predicting outcome of surgery for symptomatic hemorrhagic brainstem cavernous malformations.
Neurosurgical focus
Jul 1, 2025
OBJECTIVE: Given that resection of brainstem cavernous malformations (BSCMs) ends hemorrhaging but carries a high risk of neurological deficits, it is necessary to develop and validate a model predicting surgical outcomes. This study aimed to constru...
Extended Technical and Clinical Validation of Deep Learning-Based Brainstem Segmentation for Application in Neurodegenerative Diseases.
Human brain mapping
Feb 15, 2025
Disorders of the central nervous system, including neurodegenerative diseases, frequently affect the brainstem and can present with focal atrophy. This study aimed to (1) optimize deep learning-based brainstem segmentation for a wide range of patholo...
Deep Learning-based Approach for Brainstem and Ventricular MR Planimetry: Application in Patients with Progressive Supranuclear Palsy.
Radiology. Artificial intelligence
May 1, 2024
Purpose To develop a fast and fully automated deep learning (DL)-based method for the MRI planimetric segmentation and measurement of the brainstem and ventricular structures most affected in patients with progressive supranuclear palsy (PSP). Materi...
Rediscovery of the transcerebellar approach: improving the risk-benefit ratio in robot-assisted brainstem biopsies.
Neurosurgical focus
Jan 1, 2022
OBJECTIVE: Conventional frame-based stereotaxy through a transfrontal approach (TFA) is the gold standard in brainstem biopsies. Because of the high surgical morbidity and limited impact on therapy, brainstem biopsies are controversial. The introduct...
A case report on intensive, robot-assisted rehabilitation program for brainstem radionecrosis.
Medicine
Mar 1, 2020
INTRODUCTION: Radiotherapy is a valid treatment option for nasopharyngeal carcinoma. However, complications can occur following irradiation of the closest anatomical structures, including brainstem radionecrosis (BRN). The rehabilitation is poorly de...
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