Image segmentation: Difference between revisions

Revision as of 11:49, 19 January 2023

Revision as of 03:23, 30 January 2023

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* [[Medical imaging]],<ref>{{cite journal | last1 = Pham | first1 = Dzung L. | last2 = Xu | first2 = Chenyang | last3 = Prince | first3 = Jerry L. | year = 2000 | title = Current Methods in Medical Image Segmentation | journal = Annual Review of Biomedical Engineering | volume = 2 | pages = 315–337 | pmid = 11701515 | doi = 10.1146/annurev.bioeng.2.1.315 }}</ref><ref>{{cite journal | last1 = Forghani| first1 = M. | last2 = Forouzanfar | first2 = M.| last3 = Teshnehlab| first3 = M. | year = 2010 | title = Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation | journal = Engineering Applications of Artificial Intelligence | volume = 23 | issue = 2 | pages = 160–168 | doi = 10.1016/j.engappai.2009.10.002 }}</ref> including [[volume rendering|volume rendered]] images from [[CT scan|computed tomography]] and [[magnetic resonance imaging]].

** Locate tumors and other pathologies<ref>W. Wu, A. Y. C. Chen, L. Zhao and J. J. Corso (2014): "[https://link.springer.com/article/10.1007/s11548-013-0922-7 Brain Tumor detection and segmentation in a CRF framework with pixel-pairwise affinity and super pixel-level features]", International Journal of Computer Aided Radiology and Surgery, pp. 241–253, Vol. 9.</ref><ref>E. B. George and M. Karnan (2012): "[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.411.7411&rep=rep1&type=pdf MR Brain image segmentation using Bacteria Foraging Optimization Algorithm]", ''International Journal of Engineering and Technology'', Vol. 4.</ref>

** Measure tissue volumes<ref>{{Cite journal |last1=Ye |first1=Run Zhou |last2=Noll |first2=Christophe |last3=Richard |first3=Gabriel |last4=Lepage |first4=Martin |last5=Turcotte |first5=Éric E. |last6=Carpentier |first6=André C. |date=February 2022 |title=DeepImageTranslator: A free, user-friendly graphical interface for image translation using deep-learning and its applications in 3D CT image analysis |url=https://doi.org/10.1016/j.slast.2021.10.014 |journal=SLAS Technology |volume=27 |issue=1 |pages=76–84 |doi=10.1016/j.slast.2021.10.014 |pmid=35058205 |issn=2472-6303|doi-access=free }}</ref><ref>{{Cite journal |last1=Ye |first1=En Zhou |last2=Ye |first2=En Hui |last3=Bouthillier |first3=Maxime |last4=Ye |first4=Run Zhou |date=2022-02-18 |title=DeepImageTranslator V2: analysis of multimodal medical images using semantic segmentation maps generated through deep learning |url=https://www.biorxiv.org/content/10.1101/2021.10.12.464160v2 |language=en |pages=2021.10.12.464160 |doi=10.1101/2021.10.12.464160|s2cid=239012446 }}</ref>

** Diagnosis, study of anatomical structure<ref>{{cite journal|last1=Kamalakannan|first1=Sridharan|last2=Gururajan|first2=Arunkumar|last3=Sari-Sarraf|first3=Hamed|last4=Rodney|first4=Long|last5=Antani|first5=Sameer|title=Double-Edge Detection of Radiographic Lumbar Vertebrae Images Using Pressurized Open DGVF Snakes|journal=IEEE Transactions on Biomedical Engineering|date=17 February 2010|volume=57|issue=6|pages=1325–1334|doi=10.1109/tbme.2010.2040082|pmid=20172792|s2cid=12766600}}</ref>

** Surgery planning

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== Groups of image segmentation ==

* '''Semantic segmentation''' is an approach detecting, for every pixel, belonging class of the object.<ref>{{Cite journal|last1=Guo|first1=Dazhou|last2=Pei|first2=Yanting|last3=Zheng|first3=Kang|last4=Yu|first4=Hongkai|last5=Lu|first5=Yuhang|last6=Wang|first6=Song|date=2020|title=Degraded Image Semantic Segmentation With Dense-Gram Networks|url=https://ieeexplore.ieee.org/document/8812903|journal=IEEE Transactions on Image Processing|volume=29|pages=782–795|doi=10.1109/TIP.2019.2936111|pmid=31449020|bibcode=2020ITIP...29..782G|s2cid=201753511|issn=1057-7149|doi-access=free}}</ref> For example, when all people in a figure are segmented as one object and background as one object.

* '''Instance segmentation''' is an approach that identifies, for every pixel, a belonging instance of the object. It detects each distinct object of interest in the image.<ref>{{Cite journal|last1=Yi|first1=Jingru|last2=Wu|first2=Pengxiang|last3=Jiang|first3=Menglin|last4=Huang|first4=Qiaoying|last5=Hoeppner|first5=Daniel J.|last6=Metaxas|first6=Dimitris N.|date=July 2019|title=Attentive neural cell instance segmentation|journal=Medical Image Analysis|language=en|volume=55|pages=228–240|doi=10.1016/j.media.2019.05.004|pmid=31103790|s2cid=159038604}}</ref> For example, when each person in a figure is segmented as an individual object.

* '''Panoptic segmentation''' combines semantic and instance segmentation. Like semantic segmentation, panoptic segmentation is an approach that identifies, for every pixel, the belonging class. Unlike semantic segmentation, panoptic segmentation distinguishes different instances of the same class.<ref name="Panoptic Segmentation">{{cite arXiv|authors=Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, Piotr Dollár|title=Panoptic Segmentation|eprint=1801.00868|class=cs.CV|year=2018}}</ref>