Data Labeling for Radiology

2D and 3D radiology imaging—ultrasound, MRIs, CTs, DICOM, NIFTI and x-rays—encompasses a massive amount of data that can be harnessed by AI for medical decision support and analysis.

We offer classification services to identify one or multiple findings from a set of options and precise segmentation services to draw around regions of interest with boxes, lines or polygons. Examples include annotating vessel occlusions in non-contrast CTs and determining fetal sex from ultrasound videos.

Data Types

Ultrasound
Ultrasound
CT / MRI
CT / MRI
Ultrasound
X-Ray

Case Studies | 

Data Labeling for Radiology

Brigham and Women’s Hospital

Brigham and Women’s Hospital

Research teams at Brigham and Women's Hospital engaged Centaur Labs in their goal to automate prediction of COVID-19 disease status from point of care ultrasound, funded by a grant from the Massachusetts Life Sciences Center. With mobile-friendly, gamified labeling tools, the Brigham’s experts labeled thousands of ultrasound stills. Centaur is working closely with the Brigham’s machine learning engineering team to refine the model, which is still in development. Learn more here.

Zeta Surgical

Zeta Surgical

Zeta Surgical, a startup aimed at improving neurosurgery with robotic assistance, worked with Centaur Labs to create a training set for their hemorrhage detection algorithm. Centaur Labs segmented areas of bleed on tens of thousands of brain CT scans from hemorrhage patients. Using expert opinions from multiple labelers, Centaur Labs delivered accurate segmentations to refine Zeta Surgical’s algorithm.

 Vanderbilt University

Vanderbilt University

Research teams at Vanderbilt University are working with Centaur Labs to create datasets for future AI model development. Centaur trained labelers to segment COVID-related lung lesions in CT scans who annotated lung lesions on thousands of scans. The results from this competition will be used to create a platform to evaluate emerging methods for the segmentation and quantification of lung lesions caused by SARS-CoV-2 infection from CT images.

Customer Testimonials

We knew we couldn’t rely on individual neuroradiologists—or traditional, non-medical outsourced labeling services—to segment different kinds of hemorrhages in thousands of brain CT scans. Centaur tackled this problem quickly and accurately, enabling us to focus our time on building cutting-edge AI.

Raahil Sha
CTO
Zeta Surgical

Segmenting graft-versus-host skin disease is a complex and time consuming task. Centaur collected over 10 vetted opinions per image from their network, and combining those segmentations together produced amazingly high-quality annotations.

Centaur's mobile-first labeling tools enable us to quickly and conveniently gather results from our residents. We've accelerated our research by building and refining our ML models on COVID-19 pathologies in near real time. Centaur Labs has been a crucial partner throughout.

Andrew J. Goldsmith, MD, MBA
Director of Emergency Ultrasound, Emergency Medicine
Brigham & Women's Hospital

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