Evidence map›Paper›PMID 42414617›Full record

ReviewNature protocols2026

CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images.

Valentina Matos-Romero, Jaime Gómez-Becerril, André Forjaz, Lucie Dequiedt, Tyler Newton, Saurabh Joshi, Yu Shen, Eban Hanna, Praful Nair, Arrun Sivasubramanian and 19 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Nature protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

29 authors.

Valentina Matos-RomeroDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Jaime Gómez-BecerrilNeuroscience and Life Sciences Department, Universidad Carlos III de Madrid, Madrid, Spain.
André ForjazDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Lucie DequiedtDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Tyler NewtonData Science and AI Institute, Johns Hopkins University, Baltimore, MD, USA.
Saurabh JoshiDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Yu ShenDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Eban HannaDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Praful NairDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Arrun SivasubramanianThe Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Jenny S H WangDepartment of Biomedical Engineering, Oregon Health and Sciences University, Portland, OR, USA.
Emily L Lasse-OpsahlProgram in Cancer Biology, University of Michigan, Ann Arbor, MI, USA.
Alexander T F BellDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Diogo Fróis-VieiraDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Julianna CzumDepartment of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Charles SteenbergenThe Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Dao-Fu DaiThe Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Laura D WoodThe Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Luciane T KagoharaDepartment of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Elana J FertigInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
Marina Pasca di MaglianoDepartment of Surgery, University of Michigan, Ann Arbor, MI, USA.
Joseph J ShatzelDivision of Hematology and Medical Oncology, Oregon Health and Science University, Portland, OR, USA.
Owen J T McCartyDepartment of Biomedical Engineering, Oregon Health and Sciences University, Portland, OR, USA.
Jamie O LoDepartment of Obstetrics and Gynecology, Oregon Health and Science University, Portland, OR, USA.
Avi RosenbergThe Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Ralph H HrubanThe Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Arrate Muñoz-BarrutiaNeuroscience and Life Sciences Department, Universidad Carlos III de Madrid, Madrid, Spain.
Denis WirtzDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Ashley L KiemenDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA. kiemen@jhmi.edu.ORCID http://orcid.org/0000-0002-6281-2616

Funding

Evaluating the Safety and Efficacy of Targeting the Contact Pathway to Prevent Device Associated Thrombosis.R01HL151367 · NHLBI · OREGON HEALTH & SCIENCE UNIVERSITY · PI SHATZEL, JOSEPH JAMES · 2020 to 2024
$1.5M
NHLBI NIH HHS R01 HL151367
6 · The paper itself

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.

Identifiers

PMID42414617
PMCPMC13616135

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.