End-to-end learning of brain tissue segmentation from imperfect labeling

12/03/2016
by   Alex Fedorov, et al.
0

Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue changes. Manual segmentation defines the current gold standard in quality but is prohibitively expensive. Automatic approaches are computationally intensive, incredibly slow at scale, and error prone due to usually involving many potentially faulty intermediate steps. In order to streamline the segmentation, we introduce a deep learning model that is based on volumetric dilated convolutions, subsequently reducing both processing time and errors. Compared to its competitors, the model has a reduced set of parameters and thus is easier to train and much faster to execute. The contrast in performance between the dilated network and its competitors becomes obvious when both are tested on a large dataset of unprocessed human brain volumes. The dilated network consistently outperforms not only another state-of-the-art deep learning approach, the up convolutional network, but also the ground truth on which it was trained. Not only can the incredible speed of our model make large scale analyses much easier but we also believe it has great potential in a clinical setting where, with little to no substantial delay, a patient and provider can go over test results.

READ FULL TEXT

page 4

page 5

page 6

research
08/01/2019

Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results

A current clinical challenge is identifying limb girdle muscular dystrop...
research
01/12/2018

QuickNAT: Segmenting MRI Neuroanatomy in 20 seconds

Whole brain segmentation from structural magnetic resonance imaging is a...
research
11/01/2017

Almost instant brain atlas segmentation for large-scale studies

Large scale studies of group differences in healthy controls and patient...
research
03/29/2019

Brain Tissue Segmentation Using NeuroNet With Different Pre-processing Techniques

Automatic segmentation of brain Magnetic Resonance Imaging (MRI) images ...
research
05/31/2022

DeepDefacer: Automatic Removal of Facial Features via U-Net Image Segmentation

Recent advancements in the field of magnetic resonance imaging (MRI) hav...
research
02/26/2021

Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion

Traditional seismic processing workflows (SPW) are expensive, requiring ...
research
06/13/2022

Translating automated brain tumour phenotyping to clinical neuroimaging

Background: The complex heterogeneity of brain tumours is increasingly r...

Please sign up or login with your details

Forgot password? Click here to reset