A review on depth perception techniques in organoid images_Journal of Biomedical Engineering

Authors：

SUN Yu ¹ , HUANG Fengliang ¹ ,  ZHANG Hanwen ² , JIANG Hao ² ,  LUO Gangyin ²

1. College of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, P. R. China;
2. Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences, Engineering Laboratory of Advanced In Vitro Diagnostic Technology Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China;

Corresponding author：

ZHANG Hanwen, Email: zhanghw@sibet.ac.cn

Keywords：

Organoid image; Deep learning; Depth perception technology

DOI：

10.7507/1001-5515.202404036

Video：

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Organoids are an in vitro model that can simulate the complex structure and function of tissues in vivo. Functions such as classification, screening and trajectory recognition have been realized through organoid image analysis, but there are still problems such as low accuracy in recognition classification and cell tracking. Deep learning algorithm and organoid image fusion analysis are the most advanced organoid image analysis methods. In this paper, the organoid image depth perception technology is investigated and sorted out, the organoid culture mechanism and its application concept in depth perception are introduced, and the key progress of four depth perception algorithms such as organoid image and classification recognition, pattern detection, image segmentation and dynamic tracking are reviewed respectively, and the performance advantages of different depth models are compared and analyzed. In addition, this paper also summarizes the depth perception technology of various organ images from the aspects of depth perception feature learning, model generalization and multiple evaluation parameters, and prospects the development trend of organoids based on deep learning methods in the future, so as to promote the application of depth perception technology in organoid images. It provides an important reference for the academic research and practical application in this field.

Citation： SUN Yu, HUANG Fengliang, ZHANG Hanwen, JIANG Hao, LUO Gangyin. A review on depth perception techniques in organoid images. Journal of Biomedical Engineering, 2024, 41(5): 1053-1061. doi: 10.7507/1001-5515.202404036 Copy

1.	Zhao Z, Chen X, Dowbaj A M, et al. Organoids. Nat Rev Methods Primers, 2022, 2: 94.
2.	Du X, Chen Z, Li Q, et al. Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence. Biodes Manuf, 2023, 6(3): 319-339.
3.	Han Y, Duan X, Yang L, et al. Identification of SARS-CoV-2 inhibitors using lung and colonic organoids. Nature, 2021, 589(7841): 270-275.
4.	Kanton S, Boyle M J, He Z, et al. Organoid single-cell genomic atlas uncovers human-specific features of brain development. Nature, 2019, 574(7778): 418-422.
5.	Durkee M S, Abraham R, Clark M R, et al. Artificial intelligence and cellular segmentation in tissue microscopy images. Am J Pathol, 2021, 191(10): 1693-1701.
6.	Bai L, Wu Y, Li G, et al. AI-enabled organoids: construction, analysis, and application. Bioact Mater, 2024, 31: 525-548.
7.	Kegeles E, Naumov A, Karpulevich E A, et al. Convolutional neural networks can predict retinal differentiation in retinal organoids. Front Cell Neurosci, 2020, 14: 171.
8.	Bian X, Li G, Wang C, et al. OrgaNet: a deep learning approach for automated evaluation of organoids viability in drug screening//Bioinformatics Research and Applications: 17th International Symposium, Shenzhen, China: ISBRA, 2021: 411-423.
9.	Powell R T, Moussalli M J, Guo L, et al. deepOrganoid: A brightfield cell viability model for screening matrix-embedded organoids. SLAS Discov, 2022, 27(3): 175-184.
10.	Abdul L, Xu J, Sotra A, et al. D-CryptO: deep learning-based analysis of colon organoid morphology from brightfield images. Lab Chip, 2022, 22(21): 4118-4128.
11.	Okamoto T, Natsume Y, Doi M, et al. Integration of human inspection and artificial intelligence-based morphological typing of patient-derived organoids reveals interpatient heterogeneity of colorectal cancer. Cancer Sci, 2022, 113(8): 2693-2703.
12.	Kassis T, Hernandez-Gordillo V, Langer R, et al. OrgaQuant: human intestinal organoid localization and quantification using deep convolutional neural networks. Sci Rep, 2019, 9(1): 12479.
13.	Bremer J P, Baumdick M, Knorr M S, et al. GOAT: deep learning-enhanced generalized organoid annotation tool. BioRxiv, 2022. DOI: 10.1101/2022.09.06.506648.
14.	Abdul L, Rajasekar S, Lin D S Y, et al. Deep-LUMEN assay-human lung epithelial spheroid classification from brightfield images using deep learning. Lab Chip, 2020, 20(24): 4623-4631.
15.	Domènech-Moreno E, Brandt A, Lemmetyinen TT, et al. Tellu-an object-detector algorithm for automatic classification of intestinal organoids. Dis Model Mech, 2023, 16(3): dmm049756.
16.	Yang R, Du Y, Kwan W, et al. A quick and reliable image-based AI algorithm for evaluating cellular senescence of gastric organoids. Cancer Biol Med, 2023, 20(7): 519-536.
17.	Leng B, Jiang H, Wang B, et al. Deep-Orga: an improved deep learning-based lightweight model for intestinal organoid detection. Comput Biol Med, 2024, 169: 107847.
18.	戚枫源, 邱敏, 魏国辉. 基于深度学习的甲状腺疾病超声图像诊断研究综述. 生物医学工程学杂志, 2023, 40(5): 1027-1032.
19.	Wang X, Wu C, Zhang S, et al. A novel deep learning segmentation model for organoid-based drug screening. Front Pharmacol, 2022, 13: 1080273.
20.	Zhang S, Li L, Yu P, et al. A deep learning model for drug screening and evaluation in bladder cancer organoids. Front Oncol, 2023, 13: 1064548.
21.	Park T, Kim T K, Han Y D, et al. Development of a deep learning based image processing tool for enhanced organoid analysis. Sci Rep, 2023, 13(1): 19841.
22.	Brémond Martin C, Simon Chane C, Clouchoux C, et al. Mu-Net a light architecture for small dataset segmentation of brain organoid bright-field images. Biomedicines, 2023, 11(10): 2687.
23.	Borten M A, Bajikar S S, Sasaki N, et al. Automated brightfield morphometry of 3D organoid populations by OrganoSeg. Sci Rep, 2018, 8(1): 5319.
24.	Chen Z, Ma N, Sun X, et al. Automated evaluation of tumor spheroid behavior in 3D culture using deep learning-based recognition. Biomaterials, 2021, 272: 120770.
25.	Winkelmaier G, Parvin B. An enhanced loss function simplifies the deep learning model for characterizing the 3D organoid models. Bioinformatics, 2021, 37(18): 3084-3085.
26.	de Medeiros G, Ortiz R, Strnad P, et al. Multiscale light-sheet organoid imaging framework. Nat Commun, 2022, 13(1): 4864.
27.	Deininger L, Jung-Klawitter S, Mikut R, et al. An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids. Sci Rep, 2023, 13(1): 21231.
28.	Bian X, Li G, Wang C, et al. A deep learning model for detection and tracking in high-throughput images of organoid. Comput Biol Med, 2021, 134: 104490.
29.	Larsen B M, Kannan M, Langer L F, et al. A pan-cancer organoid platform for precision medicine. Cell Rep, 2021, 36(4): 109429.
30.	Kok R N U, Hebert L, Huelsz-Prince G, et al. OrganoidTracker: efficient cell tracking using machine learning and manual error correction. PLoS One, 2020, 15(10): e0240802.
31.	Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation//Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany: MICCAI, 2015: 234-241.
32.	Matthews J M, Schuster B, Kashaf S S, et al. OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics. PLoS Comput Biol, 2022, 18(11): e1010584.
33.	Hradecka L, Wiesner D, Sumbal J, et al. Segmentation and tracking of mammary epithelial organoids in brightfield microscopy. IEEE Trans Med Imaging, 2023, 42(1): 281-290.
34.	Du X, Cui W, Song J, et al. Sketch the organoids from birth to death-development of an intelligent orgatracker system for multi-dimensional organoid analysis and recreation. BioRxiv, 2022. DOI: 10.1101/2022.12.11.519947.
35.	Zheng X, Betjes M A, Ender P, et al. Organoid cell fate dynamics in space and time. Sci Adv, 2023, 9(33): eadd6480.
36.	Marcus G F. Deep learning: a critical appraisal. ArXiv Preprint, 2018, Arxiv: 1801.00631.
37.	Li X, Xiong H, Li X, et al. Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond. Knowl Inf Syst, 2022, 64: 3197-3234.
38.	唐江平, 周晓飞, 贺鑫, 等. 基于深度学习的新型冠状病毒肺炎诊断研究综述. 计算机工程, 2021, 47(5): 1-15.
39.	Park K, Lee J Y, Lee S Y, et al. Deep learning predicts the differentiation of kidney organoids derived from human induced pluripotent stem cells. Kidney Res Clin Pract, 2023, 42(1): 75-85.
40.	Brémond-Martin C, Simon-Chane C, Clouchoux C, et al. Brain organoid data synthesis and evaluation. Front Neurosci, 2023, 17: 1220172.
41.	Beghin A, Grenci G, Sahni G, et al. Automated high-speed 3D imaging of organoid cultures with multi-scale phenotypic quantification. Nat Methods, 2022, 19(7): 881-892.

1. Zhao Z, Chen X, Dowbaj A M, et al. Organoids. Nat Rev Methods Primers, 2022, 2: 94.
2. Du X, Chen Z, Li Q, et al. Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence. Biodes Manuf, 2023, 6(3): 319-339.
3. Han Y, Duan X, Yang L, et al. Identification of SARS-CoV-2 inhibitors using lung and colonic organoids. Nature, 2021, 589(7841): 270-275.
4. Kanton S, Boyle M J, He Z, et al. Organoid single-cell genomic atlas uncovers human-specific features of brain development. Nature, 2019, 574(7778): 418-422.
5. Durkee M S, Abraham R, Clark M R, et al. Artificial intelligence and cellular segmentation in tissue microscopy images. Am J Pathol, 2021, 191(10): 1693-1701.
6. Bai L, Wu Y, Li G, et al. AI-enabled organoids: construction, analysis, and application. Bioact Mater, 2024, 31: 525-548.
7. Kegeles E, Naumov A, Karpulevich E A, et al. Convolutional neural networks can predict retinal differentiation in retinal organoids. Front Cell Neurosci, 2020, 14: 171.
8. Bian X, Li G, Wang C, et al. OrgaNet: a deep learning approach for automated evaluation of organoids viability in drug screening//Bioinformatics Research and Applications: 17th International Symposium, Shenzhen, China: ISBRA, 2021: 411-423.
9. Powell R T, Moussalli M J, Guo L, et al. deepOrganoid: A brightfield cell viability model for screening matrix-embedded organoids. SLAS Discov, 2022, 27(3): 175-184.
10. Abdul L, Xu J, Sotra A, et al. D-CryptO: deep learning-based analysis of colon organoid morphology from brightfield images. Lab Chip, 2022, 22(21): 4118-4128.
11. Okamoto T, Natsume Y, Doi M, et al. Integration of human inspection and artificial intelligence-based morphological typing of patient-derived organoids reveals interpatient heterogeneity of colorectal cancer. Cancer Sci, 2022, 113(8): 2693-2703.
12. Kassis T, Hernandez-Gordillo V, Langer R, et al. OrgaQuant: human intestinal organoid localization and quantification using deep convolutional neural networks. Sci Rep, 2019, 9(1): 12479.
13. Bremer J P, Baumdick M, Knorr M S, et al. GOAT: deep learning-enhanced generalized organoid annotation tool. BioRxiv, 2022. DOI: 10.1101/2022.09.06.506648.
14. Abdul L, Rajasekar S, Lin D S Y, et al. Deep-LUMEN assay-human lung epithelial spheroid classification from brightfield images using deep learning. Lab Chip, 2020, 20(24): 4623-4631.
15. Domènech-Moreno E, Brandt A, Lemmetyinen TT, et al. Tellu-an object-detector algorithm for automatic classification of intestinal organoids. Dis Model Mech, 2023, 16(3): dmm049756.
16. Yang R, Du Y, Kwan W, et al. A quick and reliable image-based AI algorithm for evaluating cellular senescence of gastric organoids. Cancer Biol Med, 2023, 20(7): 519-536.
17. Leng B, Jiang H, Wang B, et al. Deep-Orga: an improved deep learning-based lightweight model for intestinal organoid detection. Comput Biol Med, 2024, 169: 107847.
18. 戚枫源, 邱敏, 魏国辉. 基于深度学习的甲状腺疾病超声图像诊断研究综述. 生物医学工程学杂志, 2023, 40(5): 1027-1032.
19. Wang X, Wu C, Zhang S, et al. A novel deep learning segmentation model for organoid-based drug screening. Front Pharmacol, 2022, 13: 1080273.
20. Zhang S, Li L, Yu P, et al. A deep learning model for drug screening and evaluation in bladder cancer organoids. Front Oncol, 2023, 13: 1064548.
21. Park T, Kim T K, Han Y D, et al. Development of a deep learning based image processing tool for enhanced organoid analysis. Sci Rep, 2023, 13(1): 19841.
22. Brémond Martin C, Simon Chane C, Clouchoux C, et al. Mu-Net a light architecture for small dataset segmentation of brain organoid bright-field images. Biomedicines, 2023, 11(10): 2687.
23. Borten M A, Bajikar S S, Sasaki N, et al. Automated brightfield morphometry of 3D organoid populations by OrganoSeg. Sci Rep, 2018, 8(1): 5319.
24. Chen Z, Ma N, Sun X, et al. Automated evaluation of tumor spheroid behavior in 3D culture using deep learning-based recognition. Biomaterials, 2021, 272: 120770.
25. Winkelmaier G, Parvin B. An enhanced loss function simplifies the deep learning model for characterizing the 3D organoid models. Bioinformatics, 2021, 37(18): 3084-3085.
26. de Medeiros G, Ortiz R, Strnad P, et al. Multiscale light-sheet organoid imaging framework. Nat Commun, 2022, 13(1): 4864.
27. Deininger L, Jung-Klawitter S, Mikut R, et al. An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids. Sci Rep, 2023, 13(1): 21231.
28. Bian X, Li G, Wang C, et al. A deep learning model for detection and tracking in high-throughput images of organoid. Comput Biol Med, 2021, 134: 104490.
29. Larsen B M, Kannan M, Langer L F, et al. A pan-cancer organoid platform for precision medicine. Cell Rep, 2021, 36(4): 109429.
30. Kok R N U, Hebert L, Huelsz-Prince G, et al. OrganoidTracker: efficient cell tracking using machine learning and manual error correction. PLoS One, 2020, 15(10): e0240802.
31. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation//Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany: MICCAI, 2015: 234-241.
32. Matthews J M, Schuster B, Kashaf S S, et al. OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics. PLoS Comput Biol, 2022, 18(11): e1010584.
33. Hradecka L, Wiesner D, Sumbal J, et al. Segmentation and tracking of mammary epithelial organoids in brightfield microscopy. IEEE Trans Med Imaging, 2023, 42(1): 281-290.
34. Du X, Cui W, Song J, et al. Sketch the organoids from birth to death-development of an intelligent orgatracker system for multi-dimensional organoid analysis and recreation. BioRxiv, 2022. DOI: 10.1101/2022.12.11.519947.
35. Zheng X, Betjes M A, Ender P, et al. Organoid cell fate dynamics in space and time. Sci Adv, 2023, 9(33): eadd6480.
36. Marcus G F. Deep learning: a critical appraisal. ArXiv Preprint, 2018, Arxiv: 1801.00631.
37. Li X, Xiong H, Li X, et al. Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond. Knowl Inf Syst, 2022, 64: 3197-3234.
38. 唐江平, 周晓飞, 贺鑫, 等. 基于深度学习的新型冠状病毒肺炎诊断研究综述. 计算机工程, 2021, 47(5): 1-15.
39. Park K, Lee J Y, Lee S Y, et al. Deep learning predicts the differentiation of kidney organoids derived from human induced pluripotent stem cells. Kidney Res Clin Pract, 2023, 42(1): 75-85.
40. Brémond-Martin C, Simon-Chane C, Clouchoux C, et al. Brain organoid data synthesis and evaluation. Front Neurosci, 2023, 17: 1220172.
41. Beghin A, Grenci G, Sahni G, et al. Automated high-speed 3D imaging of organoid cultures with multi-scale phenotypic quantification. Nat Methods, 2022, 19(7): 881-892.

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