دورية أكاديمية
Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey.
العنوان: | Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey. |
---|---|
المؤلفون: | Huang SY; Institute of Medical Science, Tzu Chi University, Hualien 97071, Taiwan.; Department of Radiation Oncology, Hualien Tzu Chi General Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan., Hsu WL; Department of Radiation Oncology, Hualien Tzu Chi General Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan.; Cancer Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan.; School of Medicine, Tzu Chi University, Hualien 97071, Taiwan., Hsu RJ; Institute of Medical Science, Tzu Chi University, Hualien 97071, Taiwan.; Cancer Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan.; School of Medicine, Tzu Chi University, Hualien 97071, Taiwan., Liu DW; Institute of Medical Science, Tzu Chi University, Hualien 97071, Taiwan.; Department of Radiation Oncology, Hualien Tzu Chi General Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan.; Cancer Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan.; School of Medicine, Tzu Chi University, Hualien 97071, Taiwan. |
المصدر: | Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2022 Nov 11; Vol. 12 (11). Date of Electronic Publication: 2022 Nov 11. |
نوع المنشور: | Journal Article; Review |
اللغة: | English |
بيانات الدورية: | Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101658402 Publication Model: Electronic Cited Medium: Print ISSN: 2075-4418 (Print) Linking ISSN: 20754418 NLM ISO Abbreviation: Diagnostics (Basel) Subsets: PubMed not MEDLINE |
أسماء مطبوعة: | Original Publication: Basel, Switzerland : MDPI AG, [2011]- |
مستخلص: | There have been major developments in deep learning in computer vision since the 2010s. Deep learning has contributed to a wealth of data in medical image processing, and semantic segmentation is a salient technique in this field. This study retrospectively reviews recent studies on the application of deep learning for segmentation tasks in medical imaging and proposes potential directions for future development, including model development, data augmentation processing, and dataset creation. The strengths and deficiencies of studies on models and data augmentation, as well as their application to medical image segmentation, were analyzed. Fully convolutional network developments have led to the creation of the U-Net and its derivatives. Another noteworthy image segmentation model is DeepLab. Regarding data augmentation, due to the low data volume of medical images, most studies focus on means to increase the wealth of medical image data. Generative adversarial networks (GAN) increase data volume via deep learning. Despite the increasing types of medical image datasets, there is still a deficiency of datasets on specific problems, which should be improved moving forward. Considering the wealth of ongoing research on the application of deep learning processing to medical image segmentation, the data volume and practical clinical application problems must be addressed to ensure that the results are properly applied. |
References: | BMC Med Inform Decis Mak. 2020 Dec 15;20(Suppl 14):317. (PMID: 33323117) J Hepatobiliary Pancreat Sci. 2021 Jan;28(1):95-104. (PMID: 32910528) PeerJ Comput Sci. 2021 Jun 29;7:e607. (PMID: 34307860) Proc SPIE Int Soc Opt Eng. 2018 Mar;10574:. (PMID: 29887665) Comput Methods Programs Biomed. 2021 Mar;200:105878. (PMID: 33308904) Sci Rep. 2017 Aug 18;7(1):8738. (PMID: 28821822) J Digit Imaging. 2020 Jun;33(3):678-684. (PMID: 32026218) IEEE Trans Pattern Anal Mach Intell. 2022 Oct;44(10):6695-6714. (PMID: 34314356) Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:3986-9. (PMID: 23366801) Sci Rep. 2019 Nov 15;9(1):16884. (PMID: 31729403) Med Phys. 2016 Apr;43(4):1882. (PMID: 27036584) Tomography. 2016 Dec;2(4):421-429. (PMID: 28105470) Sci Data. 2020 Nov 11;7(1):381. (PMID: 33177518) Radiographics. 2013 Sep-Oct;33(5):1323-41. (PMID: 24025927) Sci Rep. 2020 Jul 30;10(1):12839. (PMID: 32732963) Lancet Digit Health. 2020 Jun;2(6):e314-e322. (PMID: 33328125) IEEE Trans Med Imaging. 2020 May;39(5):1419-1429. (PMID: 31675322) Radiother Oncol. 2020 Apr;145:193-200. (PMID: 32045787) IEEE Trans Med Imaging. 2020 Jun;39(6):1856-1867. (PMID: 31841402) Emerg Radiol. 2020 Aug;27(4):367-375. (PMID: 32643070) Radiol Artif Intell. 2019 Mar 13;1(2):180014. (PMID: 33937787) Sci Rep. 2020 Apr 10;10(1):6204. (PMID: 32277135) Sci Data. 2018 Feb 20;5:180011. (PMID: 29461514) AJR Am J Roentgenol. 1978 Nov;131(5):881-5. (PMID: 101049) Sci Rep. 2022 Feb 22;12(1):2975. (PMID: 35194056) Quant Imaging Med Surg. 2021 Feb;11(2):852-857. (PMID: 33532283) Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018). 2018 Sep;11045:3-11. (PMID: 32613207) Sci Data. 2017 Sep 05;4:170117. (PMID: 28872634) IEEE Trans Med Imaging. 2015 Oct;34(10):1993-2024. (PMID: 25494501) IEEE Trans Med Imaging. 2016 May;35(5):1240-1251. (PMID: 26960222) Comput Methods Programs Biomed. 2021 Aug;207:106210. (PMID: 34130088) IEEE Trans Med Imaging. 2018 Aug;37(8):1822-1834. (PMID: 29994628) J Big Data. 2021;8(1):101. (PMID: 34306963) Med Image Anal. 2017 Jan;35:18-31. (PMID: 27310171) Invest Radiol. 2021 Jun 1;56(6):401-408. (PMID: 33930003) Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:1752-1755. (PMID: 29060226) Sensors (Basel). 2021 Mar 08;21(5):. (PMID: 33800173) IEEE Trans Pattern Anal Mach Intell. 2018 Apr;40(4):834-848. (PMID: 28463186) IEEE Trans Biomed Eng. 2017 Jul;64(7):1558-1567. (PMID: 28113302) Med Image Anal. 2017 Aug;40:172-183. (PMID: 28688283) |
معلومات مُعتمدة: | Grant TCRD-110-15, IMAR-110-01-08 Buddhist Tzu Chi Medical Foundation |
فهرسة مساهمة: | Keywords: deep learning; fully-convolutional network; medical image processing; semantic segmentation |
تواريخ الأحداث: | Date Created: 20221126 Latest Revision: 20221213 |
رمز التحديث: | 20231215 |
مُعرف محوري في PubMed: | PMC9689961 |
DOI: | 10.3390/diagnostics12112765 |
PMID: | 36428824 |
قاعدة البيانات: | MEDLINE |
تدمد: | 2075-4418 |
---|---|
DOI: | 10.3390/diagnostics12112765 |