Graph Cuts Loss to Boost Model Accuracy and Generalizability for Medical Image Segmentation

2021 ◽  
Author(s):  
Zhou Zheng ◽  
Masahiro Oda ◽  
Kensaku Mori
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Graph Cuts ◽  
Medical Image Segmentation ◽  
Model Accuracy

scholarly journals REGION MERGING VIA GRAPH-CUTS

2011 ◽  
Vol 27 (1) ◽  
pp. 39 ◽  
Author(s):  
Jean Stawiaski ◽  
Etienne Decenciére
Keyword(s):  
Image Segmentation ◽  
Minimal Surfaces ◽  
Medical Image ◽  
Graph Cuts ◽  
Medical Image Segmentation ◽  
Experimental Results ◽  
Region Merging ◽  
Specific Criterion ◽  
Watershed Transform ◽  
Efficient Approximation

In this paper, we discuss the use of graph-cuts to merge the regions of the watershed transform optimally. Watershed is a simple, intuitive and efficient way of segmenting an image. Unfortunately it presents a few limitations such as over-segmentation and poor detection of low boundaries. Our segmentation process merges regions of the watershed over-segmentation by minimizing a specific criterion using graph-cuts optimization. Two methods will be introduced in this paper. The first is based on regions histogram and dissimilarity measures between adjacent regions. The second method deals with efficient approximation of minimal surfaces and geodesics. Experimental results show that these techniques can efficiently be used for large images segmentation when a pre-computed low level segmentation is available. We will present these methods in the context of interactive medical image segmentation.

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
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