Data-Driven Interactive 3D Medical Image Segmentation Based on Structured Patch Model

2013 ◽  
pp. 196-207 ◽  
Author(s):  
Sang Hyun Park ◽  
Il Dong Yun ◽  
Sang Uk Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Data Driven ◽  
Patch Model ◽  
Interactive 3D

Interactive 3D medical image segmentation with energy-minimizing implicit functions

2011 ◽  
Vol 35 (2) ◽  
pp. 275-287 ◽  
Author(s):  
Frank Heckel ◽  
Olaf Konrad ◽  
Horst Karl Hahn ◽  
Heinz-Otto Peitgen
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Implicit Functions ◽  
Interactive 3D

Data-Driven Deep Supervision for Medical Image Segmentation

2022 ◽  
pp. 1-1
Author(s):  
Suraj Mishra ◽  
Yizhe Zhang ◽  
Danny Z. Chen ◽  
X. Sharon Hu
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Data Driven

scholarly journals Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation

2020 ◽  
Vol 22 (1) ◽  
pp. 127-153
Author(s):  
John A. Onofrey ◽  
Lawrence H. Staib ◽  
Xiaojie Huang ◽  
Fan Zhang ◽  
Xenophon Papademetris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Deep Learning ◽  
Computational Efficiency ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Data Driven ◽  
High Dimensional ◽  
Biomedical Image ◽  
Selection Of

Sparsity is a powerful concept to exploit for high-dimensional machine learning and associated representational and computational efficiency. Sparsity is well suited for medical image segmentation. We present a selection of techniques that incorporate sparsity, including strategies based on dictionary learning and deep learning, that are aimed at medical image segmentation and related quantification.

Design of Superpiexl U-Net Network for Medical Image Segmentation

2019 ◽  
Vol 31 (6) ◽  
pp. 1007 ◽  
Author(s):  
Haiou Wang ◽  
Hui Liu ◽  
Qiang Guo ◽  
Kai Deng ◽  
Caiming Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

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