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Branciforti F, Salvi M, D’Agostino F, Marzola F, Cornacchia S, De Titta MO, Mastronuzzi G, Meloni I, Moschetta M, Porciani N, Sciscenti F, Spertini A, Spilla A, Zagaria I, Deloria AJ, Deng S, Haindl R, Szakacs G, Csiszar A, Liu M, Drexler W, Molinari F, Meiburger KM. Segmentation and Multi-Timepoint Tracking of 3D Cancer Organoids from Optical Coherence Tomography Images Using Deep Neural Networks. Diagnostics (Basel) 2024; 14:1217. [PMID: 38928633 PMCID: PMC11203156 DOI: 10.3390/diagnostics14121217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/29/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024] Open
Abstract
Recent years have ushered in a transformative era in in vitro modeling with the advent of organoids, three-dimensional structures derived from stem cells or patient tumor cells. Still, fully harnessing the potential of organoids requires advanced imaging technologies and analytical tools to quantitatively monitor organoid growth. Optical coherence tomography (OCT) is a promising imaging modality for organoid analysis due to its high-resolution, label-free, non-destructive, and real-time 3D imaging capabilities, but accurately identifying and quantifying organoids in OCT images remain challenging due to various factors. Here, we propose an automatic deep learning-based pipeline with convolutional neural networks that synergistically includes optimized preprocessing steps, the implementation of a state-of-the-art deep learning model, and ad-hoc postprocessing methods, showcasing good generalizability and tracking capabilities over an extended period of 13 days. The proposed tracking algorithm thoroughly documents organoid evolution, utilizing reference volumes, a dual branch analysis, key attribute evaluation, and probability scoring for match identification. The proposed comprehensive approach enables the accurate tracking of organoid growth and morphological changes over time, advancing organoid analysis and serving as a solid foundation for future studies for drug screening and tumor drug sensitivity detection based on organoids.
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Affiliation(s)
- Francesco Branciforti
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
| | - Massimo Salvi
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
| | - Filippo D’Agostino
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
| | - Francesco Marzola
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
| | - Sara Cornacchia
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
| | - Maria Olimpia De Titta
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
| | - Girolamo Mastronuzzi
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
| | - Isotta Meloni
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
| | - Miriam Moschetta
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
| | - Niccolò Porciani
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
| | - Fabrizio Sciscenti
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
| | - Alessandro Spertini
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
| | - Andrea Spilla
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
| | - Ilenia Zagaria
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
| | - Abigail J. Deloria
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (A.J.D.); (S.D.); (R.H.); (M.L.); (W.D.)
| | - Shiyu Deng
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (A.J.D.); (S.D.); (R.H.); (M.L.); (W.D.)
| | - Richard Haindl
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (A.J.D.); (S.D.); (R.H.); (M.L.); (W.D.)
| | - Gergely Szakacs
- Center for Cancer Research, Medical University of Vienna, 1090 Vienna, Austria; (G.S.); (A.C.)
| | - Agnes Csiszar
- Center for Cancer Research, Medical University of Vienna, 1090 Vienna, Austria; (G.S.); (A.C.)
| | - Mengyang Liu
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (A.J.D.); (S.D.); (R.H.); (M.L.); (W.D.)
| | - Wolfgang Drexler
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (A.J.D.); (S.D.); (R.H.); (M.L.); (W.D.)
| | - Filippo Molinari
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
| | - Kristen M. Meiburger
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy; (F.B.); (M.S.); (F.D.); (F.M.); (S.C.); (M.O.D.T.); (G.M.); (I.M.); (M.M.); (N.P.); (F.S.); (A.S.); (A.S.); (I.Z.); (F.M.)
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Bonatti AF, Vozzi G, De Maria C. Enhancing quality control in bioprinting through machine learning. Biofabrication 2024; 16:022001. [PMID: 38262061 DOI: 10.1088/1758-5090/ad2189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/23/2024] [Indexed: 01/25/2024]
Abstract
Bioprinting technologies have been extensively studied in literature to fabricate three-dimensional constructs for tissue engineering applications. However, very few examples are currently available on clinical trials using bioprinted products, due to a combination of technological challenges (i.e. difficulties in replicating the native tissue complexity, long printing times, limited choice of printable biomaterials) and regulatory barriers (i.e. no clear indication on the product classification in the current regulatory framework). In particular, quality control (QC) solutions are needed at different stages of the bioprinting workflow (including pre-process optimization, in-process monitoring, and post-process assessment) to guarantee a repeatable product which is functional and safe for the patient. In this context, machine learning (ML) algorithms can be envisioned as a promising solution for the automatization of the quality assessment, reducing the inter-batch variability and thus potentially accelerating the product clinical translation and commercialization. In this review, we comprehensively analyse the main solutions that are being developed in the bioprinting literature on QC enabled by ML, evaluating different models from a technical perspective, including the amount and type of data used, the algorithms, and performance measures. Finally, we give a perspective view on current challenges and future research directions on using these technologies to enhance the quality assessment in bioprinting.
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Affiliation(s)
- Amedeo Franco Bonatti
- Department of Information Engineering and Research Center 'E. Piaggio', University of Pisa, Pisa, Italy
| | - Giovanni Vozzi
- Department of Information Engineering and Research Center 'E. Piaggio', University of Pisa, Pisa, Italy
| | - Carmelo De Maria
- Department of Information Engineering and Research Center 'E. Piaggio', University of Pisa, Pisa, Italy
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