Introduction to the Special Issue on Explainable Deep Learning for Medical Image Computing

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-2
Author(s):  
Yu-Dong Zhang ◽  
Juan Manuel Gorriz ◽  
Zhengchao Dong
Keyword(s):  
Deep Learning ◽  
Medical Image ◽  
Special Issue ◽  
Medical Image Computing

Image Based Brain Segmentation: From Multi-Atlas Fusion to Deep Learning

2019 ◽  
Vol 15 (5) ◽  
pp. 443-452 ◽  
Author(s):  
Xiangbo Lin ◽  
Xiaoxi Li
Keyword(s):  
Deep Learning ◽  
Brain Tissue ◽  
Medical Image ◽  
Computer Assisted ◽  
Brain Segmentation ◽  
Label Fusion ◽  
Grand Challenges ◽  
The Future ◽  
Medical Image Computing

Background: This review aims to identify the development of the algorithms for brain tissue and structure segmentation in MRI images. Discussion: Starting from the results of the Grand Challenges on brain tissue and structure segmentation held in Medical Image Computing and Computer-Assisted Intervention (MICCAI), this review analyses the development of the algorithms and discusses the tendency from multi-atlas label fusion to deep learning. The intrinsic characteristics of the winners’ algorithms on the Grand Challenges from the year 2012 to 2018 are analyzed and the results are compared carefully. Conclusion: Although deep learning has got higher rankings in the challenge, it has not yet met the expectations in terms of accuracy. More effective and specialized work should be done in the future.

scholarly journals Special issue: BVM 2009 Advances and recent developments in medical image computing

2010 ◽  
Vol 5 (6) ◽  
pp. 555-556 ◽  
Author(s):  
Heinz Handels ◽  
Hans-Peter Meinzer ◽  
Thomas M. Deserno ◽  
Thomas Tolxdorff
Keyword(s):  
Medical Image ◽  
Special Issue ◽  
Recent Developments ◽  
Medical Image Computing

scholarly journals Special Issue on “Machine Learning/Deep Learning in Medical Image Processing”

2021 ◽  
Vol 11 (23) ◽  
pp. 11483
Author(s):  
Mizuho Nishio
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Processing ◽  
Special Issue

Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL) [...]

scholarly journals Special issue: 4th MICCAI workshop on deep learning in medical image analysis

2020 ◽  
Vol 8 (5) ◽  
pp. 501-501
Author(s):  
Gustavo Carneiro ◽  
João Manuel R. S. Tavares ◽  
Andrew P. Bradley ◽  
João Paulo Papa ◽  
Vasileios Belagiannis ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Special Issue

scholarly journals Batch Normalized Convolution Neural Network for Liver Segmentation

2020 ◽  
Vol 11 (5) ◽  
pp. 21-35
Author(s):  
Fatima Abdalbagi ◽  
Serestina Viriri ◽  
Mohammed Tajalsir Mohammed
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Computer Assisted ◽  
Liver Segmentation ◽  
Learning Technique ◽  
Liver Surface ◽  
Medical Image Computing ◽  
Normalized Convolution

With the huge innovative improvement in all lifestyles, it has been important to build up the clinical fields, remembering the finding for which treatment is done; where the fruitful treatment relies upon the preoperative. Models for the preoperative, for example, planning to understand the complex internal structure of the liver and precisely localize the liver surface and its tumors; there are various algorithms proposed to do the automatic liver segmentation. In this paper, we propose a Batch Normalization After All - Convolutional Neural Network (BATA-Convnet) model to segment the liver CT images using Deep Learning Technique. The proposed liver segmentation model consists of four main steps: pre-processing, training the BATA-Convnet, liver segmentation, and the postprocessing step to maximize the result efficiency. Medical Image Computing and Computer Assisted Intervention (MICCAI) dataset and 3DImage Reconstruction for Comparison of Algorithm Database (3D-IRCAD) were used in the experimentation and the average results using MICCAI are 0.91% for Dice, 13.44% for VOE, 0.23% for RVD, 0.29mm for ASD, 1.35mm for RMSSD and 0.36mm for MaxASD. The average results using 3DIRCAD dataset are 0.84% for Dice, 13.24% for VOE, 0.16% for RVD, 0.32mm for ASD, 1.17mm for RMSSD and 0.33mm for MaxASD.

Special issue on medical image computing and systems

2014 ◽  
Vol 19 ◽  
pp. 2-3 ◽  
Author(s):  
Alex Pappachen James ◽  
Sheshadri Thiruvenkadam ◽  
Joseph Suresh Paul ◽  
Michael Braun
Keyword(s):  
Medical Image ◽  
Special Issue ◽  
Medical Image Computing
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