# Medical Image Annotation for AI

Whether generated by smartphones or regulated imaging devices, imaging data represents a significant opportunity for AI development.

Our annotation platform enables clients to structure 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

### Trusted by AI leaders across healthcare

> “We were able to improve our model dramatically - from .6 to .83 F1 score - in part, because of Centaur.ai.”  
>   
> **Fausto Milletarì**  
> Sr. AI Scientist
> [Read the story →](https://7724356.fs1.hubspotusercontent-na1.net/hubfs/7724356/Case%20Study%201-pagers/Centaur%20Labs_CaseStudy_Paige.pdf)

> “We’re excited by the high throughput and quality of annotations we get with Centaur.ai - I would definitely recommend working with them.”  
>   
> **Sarah Bettigole**  
> Head of Immunology and Data Science  
> [Read the story →](https://7724356.fs1.hubspotusercontent-na1.net/hubfs/7724356/Case%20Study%201-pagers/Centaur%20Labs_CaseStudy_Volastra.pdf)

> “We were able to use the disagreement between labelers to convert categorical labels into a continuous metric. That’s ultimately the training data we used and it was only possible because of the 20+ opinions we could gather from Centaur.ai on each piece of our data.”

> **Dr. Jason Shumake**  
> Director of Data Science  
> [Read the story →](https://7724356.fs1.hubspotusercontent-na1.net/hubfs/7724356/Case%20Study%201-pagers/Centaur%20Labs_CaseStudy_Aiberry.pdf)

## More labeling solutions

**Text**  
- Unstructured clinical notes  
- Scientific text  
- Chatbots  
- And more](https://centaur.ai/data-type/datatype-text)  
**Audio**  
- Heart auscultation  
- Lung auscultation  
- Artery auscultation  
- And more](https://centaur.ai/data-type/datatype-audio)  
**Images**  
- Ultrasound  
- External images  
- X-ray  
- And more](https://centaur.ai/data-type/datatype-images)  
**Video**  
- Surgical  
- Clinical sessions  
- And more](https://centaur.ai/data-type/datatype-video)  
.png)**Waveform**  
- EEG  
- ECG  
- PPG  
- And more](https://centaur.ai/data-type/datatype-waveform)

Accelerate your AI development today.