CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images.
Authors
Affiliations (25)
Affiliations (25)
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA.
- The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Neuroscience and Life Sciences Department, Universidad Carlos III de Madrid, Madrid, Spain.
- Bioengineering Division, Instituto de Investigación Sanitaria Gregorio Marañon, Madrid, Spain.
- Data Science and AI Institute, Johns Hopkins University, Baltimore, MD, USA.
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.
- Department of Biomedical Engineering, Oregon Health and Sciences University, Portland, OR, USA.
- Program in Cancer Biology, University of Michigan, Ann Arbor, MI, USA.
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
- Division of Hematology/Oncology, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
- Greenbaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA.
- UM-Institute for Health Computing, University of Maryland School of Medicine, North Bethesda, MD, USA.
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA.
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA.
- Division of Hematology and Medical Oncology, Oregon Health and Science University, Portland, OR, USA.
- Department of Obstetrics and Gynecology, Oregon Health and Science University, Portland, OR, USA.
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA. [email protected].
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA. [email protected].
- The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA. [email protected].
- Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA. [email protected].
- Department of Functional Anatomy and Evolution, Johns Hopkins School of Medicine, Baltimore, MD, USA. [email protected].
Abstract
Image-based machine learning tools are powerful resources for analyzing medical images, with deep learning-based semantic segmentation commonly utilized to enable the spatial quantification of structures visible in images. However, dataset generation and training of segmentation algorithms requires advanced programming skills and intricate workflows, limiting their accessibility to scientists without prior coding expertise. Here we present the step-by-step instructions to carry out automatic segmentation of medical images guided by a graphical user interface using the CODAvision algorithm. This workflow simplifies the process of semantic segmentation of microanatomical structures by enabling users to train highly customizable deep learning models without extensive coding expertise. The protocol outlines best practices for creating robust training datasets, configuring model parameters and optimizing performance across diverse biomedical image modalities. CODAvision enhances the usability of the CODA algorithm by streamlining parameter configuration, model training and performance evaluation, automatically generating quantitative results and comprehensive reports. We show the use of CODA to serial histology by demonstrating robust performance across numerous medical image modalities and diverse biological questions. We provide sample results in data types, including histology, magnetic resonance imaging and computed tomography. We demonstrate the diverse use of this tool in applications, including quantification of metastatic burden in in vivo models and deconvolution of spot-based spatial transcriptomics datasets. This protocol is designed for researchers with interest in rapid design of highly customizable semantic segmentation algorithms and a basic understanding of programming and anatomy.