Literature DB >> 17281935

Evaluation of Segmentation algorithms for Medical Imaging.

Aaron Fenster1, Bernard Chiu.   

Abstract

This paper describes an approach to be used for medical image segmentation evaluation. The process for segmenting organs and structures from medical images is gaining increased importance in the diagnosis of diseases and in guiding minimally invasive surgical and therapeutic procedures. While investigators are continuing to develop novel new segmentation approaches, little attention has been given to the development of a uniform and common framework for and performance metrics to be used in comparing different algorithms, in optimizing algorithms and in evaluating their performance. Choosing an appropriate effectiveness measure of object segmentation is a difficult task and weighting the importance of different possible performance metrics requires matching the metrics to the segmentation objectives. However, in all tasks, it is now believed that three types of metrics must be measured and reported: accuracy, precision and efficiency. In this paper, we review some of these metrics.

Entities:  

Year:  2005        PMID: 17281935     DOI: 10.1109/IEMBS.2005.1616166

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  18 in total

1.  Combining split-and-merge and multi-seed region growing algorithms for uterine fibroid segmentation in MRgFUS treatments.

Authors:  Leonardo Rundo; Carmelo Militello; Salvatore Vitabile; Carlo Casarino; Giorgio Russo; Massimo Midiri; Maria Carla Gilardi
Journal:  Med Biol Eng Comput       Date:  2015-11-03       Impact factor: 2.602

2.  Automatic segmentation of brain MR images using an adaptive balloon snake model with fuzzy classification.

Authors:  Hung-Ting Liu; Tony W H Sheu; Herng-Hua Chang
Journal:  Med Biol Eng Comput       Date:  2013-06-07       Impact factor: 2.602

3.  Combined Spline and B-spline for an improved automatic skin lesion segmentation in dermoscopic images using optimal color channel.

Authors:  A A Abbas; X Guo; W H Tan; H A Jalab
Journal:  J Med Syst       Date:  2014-06-24       Impact factor: 4.460

4.  On the evaluation of segmentation editing tools.

Authors:  Frank Heckel; Jan H Moltz; Hans Meine; Benjamin Geisler; Andreas Kießling; Melvin D'Anastasi; Daniel Pinto Dos Santos; Ashok Joseph Theruvath; Horst K Hahn
Journal:  J Med Imaging (Bellingham)       Date:  2014-11-14

5.  CT prostate segmentation based on synthetic MRI-aided deep attention fully convolution network.

Authors:  Yang Lei; Xue Dong; Zhen Tian; Yingzi Liu; Sibo Tian; Tonghe Wang; Xiaojun Jiang; Pretesh Patel; Ashesh B Jani; Hui Mao; Walter J Curran; Tian Liu; Xiaofeng Yang
Journal:  Med Phys       Date:  2019-12-03       Impact factor: 4.071

6.  Task-based evaluation of segmentation algorithms for diffusion-weighted MRI without using a gold standard.

Authors:  Abhinav K Jha; Matthew A Kupinski; Jeffrey J Rodríguez; Renu M Stephen; Alison T Stopeck
Journal:  Phys Med Biol       Date:  2012-06-20       Impact factor: 3.609

7.  [Study on the accuracy of automatic segmentation of knee CT images based on deep learning].

Authors:  Ping Song; Zheqi Fan; Xin Zhi; Zheng Cao; Shengfeng Min; Xingyu Liu; Yiling Zhang; Xiangpeng Kong; Wei Chai
Journal:  Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi       Date:  2022-05-15

8.  A method for the evaluation of thousands of automated 3D stem cell segmentations.

Authors:  P Bajcsy; M Simon; S J Florczyk; C G Simon; D Juba; M C Brady
Journal:  J Microsc       Date:  2015-08-13       Impact factor: 1.758

9.  Flexible methods for segmentation evaluation: results from CT-based luggage screening.

Authors:  Seemeen Karimi; Xiaoqian Jiang; Pamela Cosman; Harry Martz
Journal:  J Xray Sci Technol       Date:  2014       Impact factor: 1.535

10.  3D Quantitative Synthetic MRI in the Evaluation of Multiple Sclerosis Lesions.

Authors:  S Fujita; K Yokoyama; A Hagiwara; S Kato; C Andica; K Kamagata; N Hattori; O Abe; S Aoki
Journal:  AJNR Am J Neuroradiol       Date:  2021-01-07       Impact factor: 3.825

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