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Centaur - Annotate Medical Images Software
90% of all healthcare data is medical images. Whether generated by smartphones or regulated medical imaging devices, this imaging data represents a significant opportunity for AI development. Our annotation platform enables clients to structure medical images by both classifying images and segmenting regions of interest.
Use cases
Skin, face and waste images
Tag skin images for the presence and severity of lesions i.e. lacerations, psoriasis, acne or rashes. Classify skin tone for granular color matching. Classify and segment bodily wastes and fluids to determine interventions.
Ultrasound
Identify pleural and b-lines in lung ultrasounds. Segment areas of abnormal blood flow, fetal abnormalities, or gallstones.
X-ray
Identify the presence of a lesion, e.g. cavity, septal lines or broken bone. Segment the location of that lesion.
Pathology slide
Identify cellular processes e.g. mitosis, to determine mitotic rate of a cancer. Classify cells as high or low grade, to determine differentiation from tumor cells from healthy cells. Classify the presence of and segment other cellular or molecular features.
MRI, CT and PET
Identify the presence of a lesion, e.g. tumor, lung nodule, brain bleed, or area of decreased blood flow. Segment the location of that lesion.
Annotation types
- Classification
- Polygon segmentation
- Box segmentation
- Line segmentation
- Circle segmentation