Semantic segmentation of pancreatic medical images by using convolutional neural network

2022 ◽  
Vol 73 ◽  
pp. 103458
Mei-Ling Huang ◽  
Yi-Zhen Wu
2020 ◽  
Vol 23 (1) ◽  
Alejandra Márquez Herrera ◽  
Alex J. Cuadros-Vargas ◽  
Helio Pedrini

A neural network is a mathematical model that is able to perform a task automatically or semi-automatically after learning the human knowledge that we provided. Moreover, a Convolutional Neural Network (CNN) is a type of neural network that has shown to efficiently learn tasks related to the area of image analysis, such as image segmentation, whose main purpose is to find regions or separable objects within an image. A more specific type of segmentation, called semantic segmentation, guarantees that each region has a semantic meaning by giving it a label or class. Since CNNs can automate the task of image semantic segmentation, they have been very useful for the medical area, applying them to the segmentation of organs or abnormalities (tumors). This work aims to improve the task of binary semantic segmentation of volumetric medical images acquired by Magnetic Resonance Imaging (MRI) using a pre-existing Three-Dimensional Convolutional Neural Network (3D CNN) architecture. We propose a formulation of a loss function for training this 3D CNN, for improving pixel-wise segmentation results. This loss function is formulated based on the idea of adapting a similarity coefficient, used for measuring the spatial overlap between the prediction and ground truth, and then using it to train the network. As contribution, the developed approach achieved good performance in a context where the pixel classes are imbalanced. We show how the choice of the loss function for training can affect the nal quality of the segmentation. We validate our proposal over two medical image semantic segmentation datasets and show comparisons in performance between the proposed loss function and other pre-existing loss functions used for binary semantic segmentation.

2020 ◽  
Vol 1682 ◽  
pp. 012077
Tingting Li ◽  
Chunshan Jiang ◽  
Zhenqi Bian ◽  
Mingchang Wang ◽  
Xuefeng Niu

2019 ◽  
Vol 32 (7) ◽  
pp. 1949-1958 ◽  
Laigang Zhang ◽  
Zhou Sheng ◽  
Yibin Li ◽  
Qun Sun ◽  
Ying Zhao ◽  

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