medical

Skull Stripping

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

CompNet: Complementary Segmentation Network for Brain MRI Extraction

Raunak Dey, Yi Hong•Mon Mar 26 2018

This paper investigates the problem of skull stripping and proposes complementary segmentation networks (CompNets) to accurately extract the brain from T1-weighted MRI scans, for both normal and pathological brain images, resulting in the state of the art performance under the two-fold cross-validation setting.

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A survey of loss functions for semantic segmentation

Shruti Jadon•Thu Jun 25 2020

A new log-cosh dice loss function is introduced and it is showcased that certain loss functions perform well across all data-sets and can be taken as a good baseline choice in unknown data distribution scenarios.

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Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network

Ehsan Hosseini-Asl, G. Gimel'farb, A. El-Baz•Fri Jul 01 2016

To predict the Alzheimer's disease with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capturing AD biomarkers and adapt to different domain datasets, which has been validated on the CADDementia dataset.

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Training of a Skull-Stripping Neural Network with efficient data augmentation

Gabriele Valvano, Andrea Leo, N. Martini, Gianmarco Santini, D. Latta, E. Ricciardi, D. Chiappino•Wed Oct 24 2018

This work proposes a novel approach based on Convolutional Neural Networks to automatically perform the brain extraction obtaining cutting-edge performance in the NFBS public database and focuses on the efficient training of the neural network designing an effective data augmentation pipeline.

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Alzheimer's disease diagnostics by adaptation of 3D convolutional network

Ehsan Hosseini-Asl, A. El-Baz, R. Keynton•Fri Jul 01 2016

Ability of the 3D-CNN to generalize the features learnt and adapt to other domains have been validated on the ADNI dataset and experiments on the CADDementia MRI dataset with no skull-stripping preprocessing have shown it outperforms several conventional classifiers by accuracy.

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ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation

Yuemeng Li, Hongming Li, Yong Fan•Wed Feb 12 2020

An Anatomical Context-Encoding Network (ACEnet) is developed to incorporate 3D spatial and anatomical contexts in 2D convolutional neural networks (CNNs) for efficient and accurate segmentation of brain structures from MR scans.

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Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast

D. Alexander, Yael Balbastre, P. Golland, B. Fischl, J. E. Iglesias, Benjamin Billot, A. Tabari, J. Conklin, B. Edlow•Wed Dec 23 2020

SynthSR turns clinical scans of different resolution and contrast into 1 mm MPRAGEs and enables segmentation, registration, etc with existing software (e.g. FreeSurfer) Code.

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Negligible effect of brain MRI data preprocessing for tumor segmentation

B. Shirokikh, E. Kondrateva, Polina Druzhinina, A. Dalechina, S. Zolotova, A. Golanov, M. Belyaev, Anvar Kurmukov•Sun Apr 10 2022

It is suggested that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization, and that the contribution of skull-stripping in data preprocessing is almost negligible if measured in terms of estimated tumor volume.

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Convolutional Neural Networks for Skull-stripping in Brain MR Imaging using Consensus-based Silver standard Masks

Oeslle Lucena, R. Lotufo, R. Souza, Letícia Rittner, R. Frayne•Thu Apr 12 2018

The use of silver standard masks reduced the cost of manual annotation, decreased inter-intra-rater variability, and avoided CNN segmentation super-specialization towards one specific manual annotation guideline that can occur when gold standard masks are used.

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Automatic Cerebral Hemisphere Segmentation in Rat MRI with Ischemic Lesions via Attention-based Convolutional Neural Networks

Jussi Tohka, Juan Miguel Valverde, Artem Shatillo, Riccardo De Feo•Tue Aug 03 2021

MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment cerebral hemispheres in magnetic resonance (MR) volumes of rats with ischemic lesions, yielded excellent results in multiple scenarios, demonstrating its capability to reduce human workload in rat neuroimaging studies.

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