MRI segmentation Specialist

2 weeks ago

Bengaluru Karnataka India, Karnataka Biocliq AI Full time

About the Role


We’re looking for an MRI Application Specialist with strong expertise in anatomy, pathology interpretation, and hands-on segmentation. You will support MRI data curation, segmentation workflows, collaborate closely with radiologists and AI engineers, and help build products aligned with upcoming healthcare technologies.


Key Responsibilities

  • Perform manual and semi-automatic segmentation of all anatomies, especially MSK, including joints, bones, cartilage, ligaments, tendons, and soft tissues.
  • Review and interpret multi-sequence MRI datasets (T1, T2, PD, STIR, GRE, fat-sat, etc.).
  • Support MRI protocol understanding, quality checks, and dataset preparation.
  • Contribute to annotation guidelines, workflow improvement, and tool enhancement.
  • Document segmentation protocols and maintain accurate records of anatomical variations.


Skills

· Strong knowledge of anatomy across all major MRI sequences.

· Knowledge of relevant anatomical structures and pathologies across various MRI sequences.

· High precision in 2D and 3D segmentation.

· Proficiency with segmentation tools such as 3D Slicer, ITK-SNAP, Mimics, MITK, Horos, or equivalent.

· Ability to work with DICOM data and MRI-specific parameters.

· Strong visual reasoning and attention to detail.

· Ability to manage datasets and maintain consistency across volumes and sequences.


Requirements

  • Strong understanding of MRI imaging principles, contrast mechanisms, and sequence interpretation.
  • Ability to follow structured segmentation protocols with high accuracy.
  • Clear communication skills for discussions with radiologists and technical teams.
  • Comfortable handling large MSK MRI datasets and various imaging artefacts.


Education

  • Bachelor’s degree in Radiology, Medical Imaging, Biomedical Engineering, or a related field is a MUST.


Experience

  • 2–5 years of experience working with MRI datasets and performing segmentation.
  • Experience in medical imaging annotation projects or clinical imaging workflows.
  • Prior exposure to AI/ML imaging teams is a plus.

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