1
|
Piccinini F, Drudi L, Pyun JC, Lee M, Kwak B, Ku B, Carbonaro A, Martinelli G, Castellani G. Two-dimensional segmentation fusion tool: an extensible, free-to-use, user-friendly tool for combining different bidimensional segmentations. Front Bioeng Biotechnol 2024; 12:1339723. [PMID: 38357706 PMCID: PMC10865367 DOI: 10.3389/fbioe.2024.1339723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 01/11/2024] [Indexed: 02/16/2024] Open
Abstract
Introduction: In several fields, the process of fusing multiple two-dimensional (2D) closed lines is an important step. For instance, this is fundamental in histology and oncology in general. The treatment of a tumor consists of numerous steps and activities. Among them, segmenting the cancer area, that is, the correct identification of its spatial location by the segmentation technique, is one of the most important and at the same time complex and delicate steps. The difficulty in deriving reliable segmentations stems from the lack of a standard for identifying the edges and surrounding tissues of the tumor area. For this reason, the entire process is affected by considerable subjectivity. Given a tumor image, different practitioners can associate different segmentations with it, and the diagnoses produced may differ. Moreover, experimental data show that the analysis of the same area by the same physician at two separate timepoints may result in different lines being produced. Accordingly, it is challenging to establish which contour line is the ground truth. Methods: Starting from multiple segmentations related to the same tumor, statistical metrics and computational procedures could be exploited to combine them for determining the most reliable contour line. In particular, numerous algorithms have been developed over time for this procedure, but none of them is validated yet. Accordingly, in this field, there is no ground truth, and research is still active. Results: In this work, we developed the Two-Dimensional Segmentation Fusion Tool (TDSFT), a user-friendly tool distributed as a free-to-use standalone application for MAC, Linux, and Windows, which offers a simple and extensible interface where numerous algorithms are proposed to "compute the mean" (i.e., the process to fuse, combine, and "average") multiple 2D lines. Conclusions: The TDSFT can support medical specialists, but it can also be used in other fields where it is required to combine 2D close lines. In addition, the TDSFT is designed to be easily extended with new algorithms thanks to a dedicated graphical interface for configuring new parameters. The TDSFT can be downloaded from the following link: https://sourceforge.net/p/tdsft.
Collapse
Affiliation(s)
- Filippo Piccinini
- IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, Meldola, Italy
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
| | - Lorenzo Drudi
- Student, Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Jae-Chul Pyun
- Department of Materials Science and Engineering, Yonsei University, Seoul, Republic of Korea
| | - Misu Lee
- Division of Life Sciences, College of Life Science and Bioengineering, Incheon National University, Incheon, Republic of Korea
- Institute for New Drug Development, College of Life Science and Bioengineering, Incheon National University, Incheon, Republic of Korea
| | - Bongseop Kwak
- College of Medicine, Dongguk University, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Bosung Ku
- Central R&D Center, Medical and Bio Decision (MBD) Co., Ltd., Suwon, Republic of Korea
| | - Antonella Carbonaro
- Department of Computer Science and Engineering (DISI), University of Bologna, Cesena, Italy
| | - Giovanni Martinelli
- IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, Meldola, Italy
| | - Gastone Castellani
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
| |
Collapse
|
2
|
Nuernberg E, Bruch R, Hafner M, Rudolf R, Vitacolonna M. Quantitative Analysis of Whole-Mount Fluorescence-Stained Tumor Spheroids in Phenotypic Drug Screens. Methods Mol Biol 2024; 2764:311-334. [PMID: 38393603 DOI: 10.1007/978-1-0716-3674-9_20] [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] [Indexed: 02/25/2024]
Abstract
Three-dimensional cell cultures, such as spheroids or organoids, serve as important models for drug screening purposes. Optical tissue clearing (OTC) enhances the visualization of fluorescence stainings and enables in toto microscopy of 3D cell culture models. Furthermore, subsequent automated image analysis tools convert qualitative confocal image sets into quantitative data. In this chapter, we describe a detailed protocol for preparation of HT29 cancer spheroids, 3D in toto immunostaining, glycerol-based OTC, whole-mount imaging, and semi-automated downstream image processing and segmentation for nuclear image analysis using open-source software.
Collapse
Affiliation(s)
- Elina Nuernberg
- Institute of Molecular and Cell Biology, Mannheim University of Applied Sciences, Mannheim, Germany
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
| | - Roman Bruch
- Institute of Molecular and Cell Biology, Mannheim University of Applied Sciences, Mannheim, Germany
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany
| | - Mathias Hafner
- Institute of Molecular and Cell Biology, Mannheim University of Applied Sciences, Mannheim, Germany
| | - Ruediger Rudolf
- Institute of Molecular and Cell Biology, Mannheim University of Applied Sciences, Mannheim, Germany
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
| | - Mario Vitacolonna
- Institute of Molecular and Cell Biology, Mannheim University of Applied Sciences, Mannheim, Germany.
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany.
| |
Collapse
|
3
|
Malik H, Idris AS, Toha SF, Mohd Idris I, Daud MF, Azmi NL. A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations. PeerJ Comput Sci 2023; 9:e1364. [PMID: 37346656 PMCID: PMC10280419 DOI: 10.7717/peerj-cs.1364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 04/04/2023] [Indexed: 06/23/2023]
Abstract
Cell culture is undeniably important for multiple scientific applications, including pharmaceuticals, transplants, and cosmetics. However, cell culture involves multiple manual steps, such as regularly analyzing cell images for their health and morphology. Computer scientists have developed algorithms to automate cell imaging analysis, but they are not widely adopted by biologists, especially those lacking an interactive platform. To address the issue, we compile and review existing open-source cell image processing tools that provide interactive interfaces for management and prediction tasks. We highlight the prediction tools that can detect, segment, and track different mammalian cell morphologies across various image modalities and present a comparison of algorithms and unique features of these tools, whether they work locally or in the cloud. This would guide non-experts to determine which is best suited for their purposes and, developers to acknowledge what is worth further expansion. In addition, we provide a general discussion on potential implementations of the tools for a more extensive scope, which guides the reader to not restrict them to prediction tasks only. Finally, we conclude the article by stating new considerations for the development of interactive cell imaging tools and suggesting new directions for future research.
Collapse
Affiliation(s)
- Hafizi Malik
- Healthcare Engineering and Rehabilitation Research, Department of Mechatronics Engineering, International Islamic University Malaysia, Gombak, Selangor, Malaysia
| | - Ahmad Syahrin Idris
- Department of Electrical and Electronic Engineering, University of Southampton Malaysia, Iskandar Puteri, Johor, Malaysia
| | - Siti Fauziah Toha
- Healthcare Engineering and Rehabilitation Research, Department of Mechatronics Engineering, International Islamic University Malaysia, Gombak, Selangor, Malaysia
| | - Izyan Mohd Idris
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Shah Alam, Selangor, Malaysia
| | - Muhammad Fauzi Daud
- Institute of Medical Science Technology, Universiti Kuala Lumpur, Kajang, Selangor, Malaysia
| | - Nur Liyana Azmi
- Healthcare Engineering and Rehabilitation Research, Department of Mechatronics Engineering, International Islamic University Malaysia, Gombak, Selangor, Malaysia
| |
Collapse
|
4
|
Tosca EM, Ronchi D, Facciolo D, Magni P. Replacement, Reduction, and Refinement of Animal Experiments in Anticancer Drug Development: The Contribution of 3D In Vitro Cancer Models in the Drug Efficacy Assessment. Biomedicines 2023; 11:biomedicines11041058. [PMID: 37189676 DOI: 10.3390/biomedicines11041058] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
In the last decades three-dimensional (3D) in vitro cancer models have been proposed as a bridge between bidimensional (2D) cell cultures and in vivo animal models, the gold standards in the preclinical assessment of anticancer drug efficacy. 3D in vitro cancer models can be generated through a multitude of techniques, from both immortalized cancer cell lines and primary patient-derived tumor tissue. Among them, spheroids and organoids represent the most versatile and promising models, as they faithfully recapitulate the complexity and heterogeneity of human cancers. Although their recent applications include drug screening programs and personalized medicine, 3D in vitro cancer models have not yet been established as preclinical tools for studying anticancer drug efficacy and supporting preclinical-to-clinical translation, which remains mainly based on animal experimentation. In this review, we describe the state-of-the-art of 3D in vitro cancer models for the efficacy evaluation of anticancer agents, focusing on their potential contribution to replace, reduce and refine animal experimentations, highlighting their strength and weakness, and discussing possible perspectives to overcome current challenges.
Collapse
|
5
|
Morris TA, Eldeen S, Tran RDH, Grosberg A. A comprehensive review of computational and image analysis techniques for quantitative evaluation of striated muscle tissue architecture. BIOPHYSICS REVIEWS 2022; 3:041302. [PMID: 36407035 PMCID: PMC9667907 DOI: 10.1063/5.0057434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/03/2022] [Indexed: 06/16/2023]
Abstract
Unbiased evaluation of morphology is crucial to understanding development, mechanics, and pathology of striated muscle tissues. Indeed, the ability of striated muscles to contract and the strength of their contraction is dependent on their tissue-, cellular-, and cytoskeletal-level organization. Accordingly, the study of striated muscles often requires imaging and assessing aspects of their architecture at multiple different spatial scales. While an expert may be able to qualitatively appraise tissues, it is imperative to have robust, repeatable tools to quantify striated myocyte morphology and behavior that can be used to compare across different labs and experiments. There has been a recent effort to define the criteria used by experts to evaluate striated myocyte architecture. In this review, we will describe metrics that have been developed to summarize distinct aspects of striated muscle architecture in multiple different tissues, imaged with various modalities. Additionally, we will provide an overview of metrics and image processing software that needs to be developed. Importantly to any lab working on striated muscle platforms, characterization of striated myocyte morphology using the image processing pipelines discussed in this review can be used to quantitatively evaluate striated muscle tissues and contribute to a robust understanding of the development and mechanics of striated muscles.
Collapse
Affiliation(s)
| | - Sarah Eldeen
- Center for Complex Biological Systems, University of California, Irvine, California 92697-2700, USA
| | | | | |
Collapse
|
6
|
Kaseva T, Omidali B, Hippeläinen E, Mäkelä T, Wilppu U, Sofiev A, Merivaara A, Yliperttula M, Savolainen S, Salli E. Marker-controlled watershed with deep edge emphasis and optimized H-minima transform for automatic segmentation of densely cultivated 3D cell nuclei. BMC Bioinformatics 2022; 23:289. [PMID: 35864453 PMCID: PMC9306214 DOI: 10.1186/s12859-022-04827-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/07/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The segmentation of 3D cell nuclei is essential in many tasks, such as targeted molecular radiotherapies (MRT) for metastatic tumours, toxicity screening, and the observation of proliferating cells. In recent years, one popular method for automatic segmentation of nuclei has been deep learning enhanced marker-controlled watershed transform. In this method, convolutional neural networks (CNNs) have been used to create nuclei masks and markers, and the watershed algorithm for the instance segmentation. We studied whether this method could be improved for the segmentation of densely cultivated 3D nuclei via developing multiple system configurations in which we studied the effect of edge emphasizing CNNs, and optimized H-minima transform for mask and marker generation, respectively. RESULTS The dataset used for training and evaluation consisted of twelve in vitro cultivated densely packed 3D human carcinoma cell spheroids imaged using a confocal microscope. With this dataset, the evaluation was performed using a cross-validation scheme. In addition, four independent datasets were used for evaluation. The datasets were resampled near isotropic for our experiments. The baseline deep learning enhanced marker-controlled watershed obtained an average of 0.69 Panoptic Quality (PQ) and 0.66 Aggregated Jaccard Index (AJI) over the twelve spheroids. Using a system configuration, which was otherwise the same but used 3D-based edge emphasizing CNNs and optimized H-minima transform, the scores increased to 0.76 and 0.77, respectively. When using the independent datasets for evaluation, the best performing system configuration was shown to outperform or equal the baseline and a set of well-known cell segmentation approaches. CONCLUSIONS The use of edge emphasizing U-Nets and optimized H-minima transform can improve the marker-controlled watershed transform for segmentation of densely cultivated 3D cell nuclei. A novel dataset of twelve spheroids was introduced to the public.
Collapse
Affiliation(s)
- Tuomas Kaseva
- HUS Medical Imaging Center, Radiology, Helsinki University Hospital and University of Helsinki, P.O. Box 340, FI-00290, Helsinki, Finland
| | - Bahareh Omidali
- Department of Physics, University of Helsinki, P.O. Box 64, FI-00014, Helsinki, Finland
| | - Eero Hippeläinen
- Department of Physics, University of Helsinki, P.O. Box 64, FI-00014, Helsinki, Finland.,HUS Medical Imaging Centre, Clinical Physiology and Nuclear Medicine, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Teemu Mäkelä
- HUS Medical Imaging Center, Radiology, Helsinki University Hospital and University of Helsinki, P.O. Box 340, FI-00290, Helsinki, Finland.,Department of Physics, University of Helsinki, P.O. Box 64, FI-00014, Helsinki, Finland
| | - Ulla Wilppu
- HUS Medical Imaging Center, Radiology, Helsinki University Hospital and University of Helsinki, P.O. Box 340, FI-00290, Helsinki, Finland
| | - Alexey Sofiev
- HUS Medical Imaging Center, Radiology, Helsinki University Hospital and University of Helsinki, P.O. Box 340, FI-00290, Helsinki, Finland.,Department of Physics, University of Helsinki, P.O. Box 64, FI-00014, Helsinki, Finland
| | - Arto Merivaara
- Division of Pharmaceutical Biosciences, Faculty of Pharmacy, Centre for Drug Research, University of Helsinki, Helsinki, Finland
| | - Marjo Yliperttula
- Division of Pharmaceutical Biosciences, Faculty of Pharmacy, Centre for Drug Research, University of Helsinki, Helsinki, Finland
| | - Sauli Savolainen
- HUS Medical Imaging Center, Radiology, Helsinki University Hospital and University of Helsinki, P.O. Box 340, FI-00290, Helsinki, Finland.,Department of Physics, University of Helsinki, P.O. Box 64, FI-00014, Helsinki, Finland
| | - Eero Salli
- HUS Medical Imaging Center, Radiology, Helsinki University Hospital and University of Helsinki, P.O. Box 340, FI-00290, Helsinki, Finland.
| |
Collapse
|
7
|
Hollandi R, Moshkov N, Paavolainen L, Tasnadi E, Piccinini F, Horvath P. Nucleus segmentation: towards automated solutions. Trends Cell Biol 2022; 32:295-310. [DOI: 10.1016/j.tcb.2021.12.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 11/30/2021] [Accepted: 12/14/2021] [Indexed: 11/25/2022]
|
8
|
Piccinini F, Balassa T, Carbonaro A, Diosdi A, Toth T, Moshkov N, Tasnadi EA, Horvath P. Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates. Comput Struct Biotechnol J 2020; 18:1287-1300. [PMID: 32612752 PMCID: PMC7303562 DOI: 10.1016/j.csbj.2020.05.022] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 05/22/2020] [Accepted: 05/23/2020] [Indexed: 12/25/2022] Open
Abstract
Today, we are fully immersed into the era of 3D biology. It has been extensively demonstrated that 3D models: (a) better mimic the physiology of human tissues; (b) can effectively replace animal models; (c) often provide more reliable results than 2D ones. Accordingly, anti-cancer drug screenings and toxicology studies based on multicellular 3D biological models, the so-called "-oids" (e.g. spheroids, tumoroids, organoids), are blooming in the literature. However, the complex nature of these systems limit the manual quantitative analyses of single cells' behaviour in the culture. Accordingly, the demand for advanced software tools that are able to perform phenotypic analysis is fundamental. In this work, we describe the freely accessible tools that are currently available for biologists and researchers interested in analysing the effects of drugs/treatments on 3D multicellular -oids at a single-cell resolution level. In addition, using publicly available nuclear stained datasets we quantitatively compare the segmentation performance of 9 specific tools.
Collapse
Affiliation(s)
- Filippo Piccinini
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Cancer Research Hospital, Meldola, FC, Italy
| | - Tamas Balassa
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
| | - Antonella Carbonaro
- Department of Computer Science and Engineering, University of Bologna, Italy
| | - Akos Diosdi
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Biology, University of Szeged, Hungary
| | - Timea Toth
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Biology, University of Szeged, Hungary
| | - Nikita Moshkov
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Interdisciplinary Medicine, University of Szeged, Hungary
- National Research University Higher School of Economics, Moscow, Russia
| | - Ervin A. Tasnadi
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Computer Science, University of Szeged, Hungary
| | - Peter Horvath
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Single-Cell Technologies Ltd., Szeged, Hungary
| |
Collapse
|
9
|
Tasnadi EA, Toth T, Kovacs M, Diosdi A, Pampaloni F, Molnar J, Piccinini F, Horvath P. 3D-Cell-Annotator: an open-source active surface tool for single-cell segmentation in 3D microscopy images. Bioinformatics 2020; 36:2948-2949. [PMID: 31950986 PMCID: PMC7203751 DOI: 10.1093/bioinformatics/btaa029] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2019] [Revised: 12/30/2019] [Accepted: 01/15/2020] [Indexed: 11/14/2022] Open
Abstract
SUMMARY Segmentation of single cells in microscopy images is one of the major challenges in computational biology. It is the first step of most bioimage analysis tasks, and essential to create training sets for more advanced deep learning approaches. Here, we propose 3D-Cell-Annotator to solve this task using 3D active surfaces together with shape descriptors as prior information in a semi-automated fashion. The software uses the convenient 3D interface of the widely used Medical Imaging Interaction Toolkit (MITK). Results on 3D biological structures (e.g. spheroids, organoids and embryos) show that the precision of the segmentation reaches the level of a human expert. AVAILABILITY AND IMPLEMENTATION 3D-Cell-Annotator is implemented in CUDA/C++ as a patch for the segmentation module of MITK. The 3D-Cell-Annotator enabled MITK distribution can be downloaded at: www.3D-cell-annotator.org. It works under Windows 64-bit systems and recent Linux distributions even on a consumer level laptop with a CUDA-enabled video card using recent NVIDIA drivers. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Ervin A Tasnadi
- Synthetic and System Biology Unit, Biological Research Centre (BRC), Szeged H-6726, Hungary
| | - Timea Toth
- Synthetic and System Biology Unit, Biological Research Centre (BRC), Szeged H-6726, Hungary
| | - Maria Kovacs
- Synthetic and System Biology Unit, Biological Research Centre (BRC), Szeged H-6726, Hungary
| | - Akos Diosdi
- Synthetic and System Biology Unit, Biological Research Centre (BRC), Szeged H-6726, Hungary
| | - Francesco Pampaloni
- Buchmann Institute for Molecular Life Sciences (BMLS), Goethe University of Frankfurt, DE-60438 Frankfurt, Germany
| | - Jozsef Molnar
- Synthetic and System Biology Unit, Biological Research Centre (BRC), Szeged H-6726, Hungary
| | - Filippo Piccinini
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, I-47014 Meldola (FC), Italy
| | - Peter Horvath
- Synthetic and System Biology Unit, Biological Research Centre (BRC), Szeged H-6726, Hungary
- Institute for Molecular Medicine Finland University of Helsinki, FI-00014 Helsinki, Finland
- Single-Cell Technologies Ltd, H-6726 Szeged, Hungary
| |
Collapse
|
10
|
Piccinini F, Tesei A, Zanoni M, Bevilacqua A. ReViMS: Software tool for estimating the volumes of 3-D multicellular spheroids imaged using a light sheet fluorescence microscope. Biotechniques 2017; 63:227-229. [DOI: 10.2144/000114609] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2017] [Accepted: 09/27/2017] [Indexed: 11/23/2022] Open
Abstract
Cancer 3-D spheroids are widely used to test drugs and radiotherapy treatments. These 3-D cell clusters range from tens to hundreds of micrometers in size, with shapes that typically differ from a perfect sphere. Change in spheroid volume is one of the most important parameters for evaluating treatment efficacy, and using light sheet fluorescence microscopes (LSFM), optical sections of samples in that size range can be obtained. However, there remains a lack of validated methods for quantifying the volumes of 3-D multicellular aggregates. Here, we present Reconstruction and Visualization from Multiple Sections (ReViMS), an open-source, user-friendly software for automatically segmenting z-stacks of fluorescence images and estimating the volumes of 3-D multicellular spheroids. To assess the precision and accuracy of the volume estimates obtained with ReViMS, we used several cancer spheroids imaged with LSFM. Both the precision and accuracy were >95%, demonstrating the effectiveness of ReViMS.
Collapse
Affiliation(s)
- Filippo Piccinini
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) s.r.l., IRCCS, Biosciences Laboratory, Meldola, Italy
| | - Anna Tesei
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) s.r.l., IRCCS, Biosciences Laboratory, Meldola, Italy
| | - Michele Zanoni
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) s.r.l., IRCCS, Biosciences Laboratory, Meldola, Italy
| | - Alessandro Bevilacqua
- Advanced Research Center on Electronic Systems (ARCES) for Information and Communication Technologies “E. De Castro,” University of Bologna, Italy
- Department of Computer Science and Engineering (DISI), University of Bologna, Italy
| |
Collapse
|