PMID- 34181310
OWN - NLM
STAT- MEDLINE
DCOM- 20211125
LR  - 20211125
IS  - 2040-7947 (Electronic)
IS  - 2040-7939 (Linking)
VI  - 37
IP  - 9
DP  - 2021 Sep
TI  - Improved particle swarm optimized deep convolutional neural network with 
      super-pixel clustering for multiple sclerosis lesion segmentation in brain MRI 
      imaging.
PG  - e3506
LID - 10.1002/cnm.3506 [doi]
AB  - A central nervous system (CNS) disease affecting the insulating myelin sheaths 
      around the brain axons is called multiple sclerosis (MS). In today's world, MS is 
      extensively diagnosed and monitored using the MRI, because of the structural MRI 
      sensitivity in dissemination of white matter lesions with respect to space and 
      time. The main aim of this study is to propose Multiple Sclerosis Lesion 
      Segmentation in Brain MRI imaging using Optimized Deep Convolutional Neural 
      Network and Super-pixel Clustering. Three stages included in the proposed 
      methodology are: (a) preprocessing, (b) segmentation of super-pixel, and (c) 
      classification of super-pixel. In the first stage, image enhancement and skull 
      stripping is done through performing a preprocessing step. In the second stage, 
      the MS lesion and Non-MS lesion regions are segmented through applying SLICO 
      algorithm over each slice of the volume. In the fourth stage, a CNN training and 
      classification is performed using this segmented lesion and non-lesion regions. 
      To handle this complex task, a newly developed Improved Particle Swarm 
      Optimization (IPSO) based optimized convolutional neural network classifier is 
      applied. On clinical MS data, the approach exhibits a significant increase in the 
      accuracy segmenting of WM lesions when compared with the rest of evaluated 
      methods.
CI  - (c) 2021 John Wiley & Sons Ltd.
FAU - Krishna Priya, R
AU  - Krishna Priya R
AUID- ORCID: 0000-0001-8968-7862
AD  - Department of Electrical and Communication Engineering, National University of 
      Science and Technology, Oman.
FAU - Chacko, Susamma
AU  - Chacko S
AD  - Department of Quality Enhancement and Assurance, National University of Science 
      and Technology, Oman.
LA  - eng
PT  - Journal Article
DEP - 20210810
PL  - England
TA  - Int J Numer Method Biomed Eng
JT  - International journal for numerical methods in biomedical engineering
JID - 101530293
SB  - IM
MH  - Brain/diagnostic imaging
MH  - Cluster Analysis
MH  - Humans
MH  - Image Processing, Computer-Assisted
MH  - Magnetic Resonance Imaging
MH  - *Multiple Sclerosis/diagnostic imaging
MH  - Neural Networks, Computer
OTO - NOTNLM
OT  - IPSO
OT  - MRI
OT  - convolutional neural network
OT  - lesion
OT  - multiple sclerosis
OT  - super-pixel
EDAT- 2021/06/29 06:00
MHDA- 2021/11/26 06:00
CRDT- 2021/06/28 12:38
PHST- 2021/02/09 00:00 [revised]
PHST- 2020/03/31 00:00 [received]
PHST- 2021/03/12 00:00 [accepted]
PHST- 2021/06/29 06:00 [pubmed]
PHST- 2021/11/26 06:00 [medline]
PHST- 2021/06/28 12:38 [entrez]
AID - 10.1002/cnm.3506 [doi]
PST - ppublish
SO  - Int J Numer Method Biomed Eng. 2021 Sep;37(9):e3506. doi: 10.1002/cnm.3506. Epub 
      2021 Aug 10.