Clinical researchers and practitioners can only be empowered to fully utilize images and associated meta-data with methods from informatics.
- ~1500 different imaging studies
- Many distinct imaging modalities (e.g., DR, CT, MR, US, NM)
- Visualizing a variety of body regions (e.g., head, chest, extremities, abdomen)
- Each containing dozens of organs and body parts (e.g., liver, pancreas, kidney)
- Each affected by hundreds of diseases (Total: ~23,000 conditions)
- Typically 12- or 16-bit gray scale, rather than color images.
- 3D volumetric data sets, sometimes time as 4th dimension.
- Multi-channel data (e.g., MR T1-weighing, T2-weighting, flow-sensitive, post-contrast).
- Differential diagnosis varies by anatomic structure, requiring anatomic segmentation.
These challenges are further complicated by the sheer size of the data that is available at Stanford Medicine (2016):
The Langlotzlab has several projects that are testing these algorithms to assist with tasks relevant to diagnostic radiology: