Medical image understanding is where speech and language modeling was in 1980. Back then there were few speech, language and text understanding systems. Most of the greatest work had been focusing on the digital and acoustic stream; little progress had been made on the linguistics and structural representation of the language itself. This has all changed, with the entire focus moving away from the acoustic models to the higher level representations of information. Analogously, today in imaging there is a plethora of magnificent imaging devices of all kinds, at all scales, at all prices. However, there is little in the way of image understanding systems, systems which bring value not from the measured picture but from knowledge bases in the world. Think about it, in the context of Medical image reconstruction, could it really be that there is more information from a single MRI scan than in all of the books that Neuroscientists and Radiologists have catalogued since the Renaissance era of da Vinci and Michelangelo? Why then are there no examples of medical imaging devices which inject such information into their modality.
The difficulty has been that biological shape is exquisitely variable, with few methods for computing or measuring and computing the closeness of the geometric representation of normal and abnormal anatomical and biological structures. Without such “parsing algorithms” there are no structured ways to build models of real-world knowledge, or for inserting book knowledge into the medical image understanding algorithms. This is all changing now with the emergence of Computational Anatomy in the field of Medical Imaging. Computational Anatomy, like Computational Linguistics, allows for the semantic representation of brains into their parts and connectivities.
Biomedical engineering students Manisha Aggarwal, Kwame Kutten, Daniel Tward, Yajing Zhang, are currently engaged in the characterization of the human populations, studying the functional and structural characteristics of the human brain. Disease populations are being studied associated to Huntington’s disease, dementia, bipolar disorder, schizophrenia and epilepsy. We are currently hard at work building brain clouds for delivering image understanding algorithms associated to neuropsychiatric illness.
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- White KL, Raffi F, Miller MD. Resistance Analyses of Integrase Strand Transfer Inhibitors within Phase 3 Clinical Trials of Treatment-Naive Patients. Viruses. 2014 Jul 22;6(7):2858-79. doi: 10.3390/v6072858. PMID: 25054884
2. Zhang Y, Chang L, Ceritoglu C, Skranes J, Ernst T, Mori S, Miller MI, Oishi K. A Bayesian approach to the creation of a study-customized neonatal brain atlas. Neuroimage. 2014 Jul 12. pii: S1053-8119(14)00573-4. doi: 10.1016/j.neuroimage.2014.07.001. [Epub ahead of print] PMID: 25026155
3. Lim JW, Dillon J, Miller M. Proteomic and genomic studies of non-alcoholic fatty liver disease - clues in the pathogenesis. World J Gastroenterol. 2014 Jul 14;20(26):8325-8340. Review. PMID: 25024592
4. Wolfe LL, Johnson HE, Fisher MC, Sirochman MA, Kraft B, Miller MW. Use of Acepromazine and Medetomidine in Combination for Sedation and Handling of Rocky Mountain Elk (Cervus elaphus nelsoni) and Black Bears (Ursus americanus). J Wildl Dis. 2014 Jul 11. [Epub ahead of print] PMID: 25014907
5. Sutradhar A, Park J, Carrau D, Miller MJ. Experimental validation of 3D printed patient-specific implants using digital image correlation and finite element analysis. Comput Biol Med. 2014 Jun 12;52C:8-17. doi: 10.1016/j.compbiomed.2014.06.002. [Epub ahead of print] PMID: 24992729