Fast and robust segmentation of the striatum using deep convolutional neural networks.

By Hongyoon Choi, Kyong Hwan Jin. 2016

68 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Neurology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Arvid Lundervold
2. B. van Ginneken

In Computer Vision and Pattern Recognition

1. Geert J. S. Litjens
2. J. A. van der Laak
3. Francesco Ciompi
4. Clara I. Sánchez
5. Arnaud Arindra Adiyoso Setio
6. Arvid Lundervold
7. Mohsen Ghafoorian
8. Babak Ehteshami Bejnordi

In Neurology

1. Arvid Lundervold
2. B. van Ginneken
3. Dan Boneh
4. Florian Tramèr

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, background): “example acute ischemic lesion segmentation in DWI [262]; brain tumor segmentation [263]; segmentation of the striatum [264]; segmentation of organs-at-risks in head and neck CT images [265]; and fully automated segmentation of polycystic kidneys [266]; deformable segmentation of the prostate [267]; ”
• A survey on deep learning in medical image analysis (2017, methodology): “de Brebisson and Montana (2015) CNN Anatomical segmentation; fusing multi-scale 2D patches with a 3D patch using a CNN Choi and Jin (2016) CNN Striatum segmentation; Two-stage (global/local) approximations with 3D CNNs”
• Deep Learning and Medical Diagnosis: A Review of Literature (2018, unknown): “[42] CNN MRI Segmentation of the striatum; two serial CNN architectures were used; the speed and accuracy of this approach makes it adequate for application in neuroscience and other clinical fields.”