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Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound [article]

Fausto Milletari, Seyed-Ahmad Ahmadi, Christine Kroll, Annika Plate, Verena Rozanski, Juliana Maiostre, Johannes Levin, Olaf Dietrich, Birgit Ertl-Wagner, Kai Bötzel, Nassir Navab
2016 arXiv   pre-print
We show that this learning-based segmentation method is robust, multi-region, flexible and can be easily adapted to different modalities.  ...  We show results on both MRI and transcranial US volumes depicting respectively 26 regions of the basal ganglia and the midbrain.  ...  We gratefully acknowledge the support of NVIDIA Corporation in donating a "Tesla K40" GPU for this study.  ... 
arXiv:1601.07014v3 fatcat:gjqsvh7xvjb3veqqfeo6baz7fi

Weakly-Supervised White and Grey Matter Segmentation in 3D Brain Ultrasound [article]

Beatrice Demiray, Julia Rackerseder, Stevica Bozhinoski, Nassir Navab
2019 arXiv   pre-print
Although the segmentation of brain structures in ultrasound helps initialize image based registration, assist brain shift compensation, and provides interventional decision support, the task of segmenting  ...  Our proposed methodology sets an unparalleled standard for white and grey matter segmentation in 3D intracranial ultrasound.  ...  We gratefully acknowledge the support of the GPU grant program from NVIDIA.  ... 
arXiv:1904.05191v3 fatcat:fsivxptf5jgornnyouhkzqbqqu

Automatic detection of lumen and media in the IVUS images using U-Net with VGG16 Encoder [article]

Chirag Balakrishna, Sarshar Dadashzadeh, Sara Soltaninejad
2018 arXiv   pre-print
In this paper, we investi- gate the effectiveness of Convolutional Neural Networks including U-Net to segment ultrasound scans of arteries.  ...  Intravascular Ultrasound (IVUS) has been recognized as power- ful imaging technology which captures the real time and high resolution images of the coronary arteries and can be used for the analysis of  ...  From our literature review the authors [8] who implemented a Hough-CNN to segment MRI and ultrasound modalities of the brain say that "deeper neural networks can work very well with small datasets".  ... 
arXiv:1806.07554v1 fatcat:wzamzjytera23el3cfufmagilu

Brain Image Segmentation in Recent Years: A Narrative Review

Ali Fawzi, Anusha Achuthan, Bahari Belaton
2021 Brain Sciences  
From this review, it is found that deep learning-based and hybrid-based metaheuristic approaches are more efficient for the reliable segmentation of brain tumors.  ...  This paper aims to present a critical review of the recent trend in segmentation and classification methods for brain magnetic resonance images.  ...  The present review suggests that deep learning-based and hybrid-based metaheuristic methods are more efficient for the reliable segmentation of brain tumors.  ... 
doi:10.3390/brainsci11081055 fatcat:cdie3nuxzzfevoynik3iqtenli

Derin Öğrenme Araştırma Alanlarının Literatür Taraması

M. Mutlu Yapıcı, Adem Tekerek, Nurettin Topaloğlu
2019 Gazi Mühendislik Bilimleri Dergisi  
In the present day, Deep learning methods have reached better results than humans in object recognition.  ...  In this study, it is presented important knowledge to guide about DL models and challenging topics that can be used in DL for researchers.  ...  [33] proposed Hough-CNN that is a novel segmentation approach based on a voting strategy for the segmentation of deep brain regions in MRI and ultrasound.  ... 
doi:10.30855/gmbd.2019.03.01 fatcat:2sv7dg7elrfqppcjx5otzmb7pi

Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection [article]

Zizhao Zhang and Fuyong Xing and Hai Su and Xiaoshuang Shi and Lin Yang
2017 arXiv   pre-print
The fast growing deep learning technologies have become the main solution of many machine learning problems for medical image analysis.  ...  Deep convolution neural networks (CNNs), as one of the most important branch of the deep learning family, have been widely investigated for various computer-aided diagnosis tasks including long-term problems  ...  Improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function.  ... 
arXiv:1708.07281v1 fatcat:kdplgrjf4zaurcdbszcoeinktm