Radiologic Technologist Job at iMerit Technology in New York

Posted 7 days ago

imerit technologyNew York (NY)

Radiologic Technologists and Technicians Diagnostic Imaging Centers

SENIORITY

Senior

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About the role

We are seeking experienced, US-based Radiology Technicians to participate in a medical image segmentation annotation project. This project involves the precise segmentation of lesions identified in CT and MRI studies as part of a rigorous, multi-reader data labeling workflow designed to produce high-quality, adjudicated ground-truth masks for use in AI/ML model development. The project encompasses a total of 480 lesions — 240 CT and 240 MRI — each of which will be independently segmented by three (3) qualified radiology technicians. All completed segmentations will subsequently be reviewed, adjudicated, and if necessary corrected by a US Board-Certified (USBC) Radiologist. Annotation Workflow Step 1: Lesion Selection A US Board-Certified Radiologist will identify and select each lesion for segmentation across all CT and MRI cases. Technicians will work exclusively on pre-identified lesions; independent lesion discovery is not required. Step 2: Independent Segmentation (Technician Task) Each of the 480 lesions will be independently segmented by three (3) US-based Radiology Technicians. Technicians will:

Review the imaging study containing the designated lesion.
Perform a careful, slice-by-slice segmentation of the specified lesion boundary.
Complete work independently, without reference to other technicians' annotations.
Submit segmentation masks within the designated annotation platform and within agreed

turnaround timelines. Step 3: Consensus & Radiologist Adjudication Following the collection of three independent segmentation masks per lesion, a US Board Certified Radiologist will:

Review the consensus mask derived from the three submissions.
Assess segmentation accuracy and agreement.
Make final adjustments to the segmentation where clinically necessary.

The adjudicated mask will serve as the final ground-truth annotation for the lesion.

Key Responsibilities:

Accurately segment lesion boundaries in CT and MRI imaging datasets using the provided annotation platform.
Adhere strictly to the segmentation guidelines and quality standards established by the project radiologist team.
Complete assigned segmentations independently without collaboration with fellow annotators assigned to the same case.
Maintain a high level of anatomical accuracy and consistency in delineating lesion margins across imaging planes.
Meet project deadlines and turnaround time requirements for each batch of assigned lesions.
Flag ambiguous or technically challenging cases to the project supervisor promptly.
Participate in calibration exercises or training sessions as required to ensure annotation consistency.

Required Qualifications:

Active licensure or certification as a Radiology Technician in the United States (e.g., ARRT certification in Radiography, CT, or MRI).
Minimum of 2 years of hands-on clinical or research experience working with CT and/or MRI imaging.
Demonstrated proficiency in reading and interpreting cross-sectional imaging (CT and/or MRI).
Experience with or ability to quickly learn medical image annotation or segmentation software platforms.
Strong attention to detail, with the ability to work with precision and consistency across large imaging datasets.
Ability to work independently in a remote environment and meet project milestones.
Reliable high-speed internet connection and a suitable workstation for reviewing DICOM-quality imaging data.

Preferred Qualifications:

ARRT advanced certification in CT (CT) or MRI (MR).
Prior experience in medical imaging annotation, radiology AI data projects, or clinical research image analysis.
Familiarity with DICOM imaging format and annotation tools such as ITK-SNAP, 3D Slicer, MD.ai, Labelbox, or similar platforms.
Experience with oncologic or lesion-based imaging studies.
Background in cross-sectional body imaging, neuroradiology, or musculoskeletal imaging is a plus.

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