Evaluation of threshold and gradient based 18F-fluoro-deoxy-2-glucose hybrid positron emission tomographic image segmentation methods for liver tumor delineation

2014 ◽  
Vol 4 (4) ◽  
pp. 217-225 ◽  
Author(s):  
Cem Altunbas ◽  
Christopher Howells ◽  
Michelle Proper ◽  
Krishna Reddy ◽  
Gregory Gan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Liver Tumor ◽  
Tomographic Image ◽  
Positron Emission Tomographic ◽  
Tumor Delineation ◽  
Positron Emission ◽  
Segmentation Methods ◽  
Gradient Based

scholarly journals Joint Segmentation Methods of Tumor Delineation in PET – CT Images: A Review

2018 ◽  
Vol 7 (3.32) ◽  
pp. 137
Author(s):  
Farli Rossi ◽  
Ashrani Aizzuddin Abd Rahni
Keyword(s):  
Image Segmentation ◽  
Ct Images ◽  
Basic Knowledge ◽  
Low Contrast ◽  
Tumor Delineation ◽  
Innovative Methods ◽  
Pet Ct ◽  
Segmentation Methods ◽  
Disease Staging ◽  
Puzzle Task

Segmentation is one of the crucial steps in applications of medical diagnosis. The accurate image segmentation method plays an important role in proper detection of disease, staging, diagnosis, radiotherapy treatment planning and monitoring. In the advances of image segmentation techniques, joint segmentation of PET-CT images has increasingly received much attention in the field of both clinic and image processing. PET - CT images have become a standard method for tumor delineation and cancer assessment. Due to low spatial resolution in PET and low contrast in CT images, automated segmentation of tumor in PET - CT images is a well-known puzzle task. This paper attempted to describe and review four innovative methods used in the joint segmentation of functional and anatomical PET - CT images for tumor delineation. For the basic knowledge, the state of the art image segmentation methods were briefly reviewed and fundamental of PET and CT images were briefly explained. Further, the specific characteristics and limitations of four joint segmentation methods were critically discussed.  

F-18 FDG Positron Emission Tomographic Image of an Aortic Aneurysmal Thrombus

2002 ◽  
Vol 27 (3) ◽  
pp. 213-214 ◽  
Author(s):  
SUBHA RAMAN ◽  
RODOLFO NÚÑEZ ◽  
C. OLIVER WONG ◽  
HOWARD J. DWORKIN
Keyword(s):  
Tomographic Image ◽  
Positron Emission Tomographic ◽  
Positron Emission

Positron emission tomographic image measurement in schizophrenia and affective disorders

1984 ◽  
Vol 15 (S1) ◽  
pp. 157-169 ◽  
Author(s):  
Monte S. Buchsbaum ◽  
J. Cappelletti ◽  
Ron Ball ◽  
E. Hazlett ◽  
A. C. King ◽  
...  
Keyword(s):  
Affective Disorders ◽  
Tomographic Image ◽  
Positron Emission Tomographic ◽  
Image Measurement ◽  
Positron Emission

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

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