How Well Do Self-Supervised Models Transfer to Medical Imaging?

Self-supervised learning approaches have seen success transferring between similar medical imaging datasets, however there has been no large scale attempt to compare the transferability of self-supervised models against each other on medical images. In this study, we compare the generalisability of seven self-supervised models, two of which were trained in-domain, against supervised baselines across eight different medical datasets. We find that ImageNet pretrained self-supervised models are more generalisable than their supervised counterparts, scoring up to 10% better on medical classification tasks. The two in-domain pretrained models outperformed other models by over 20% on in-domain tasks, however they suffered significant loss of accuracy on all other tasks. Our investigation of the feature representations suggests that this trend may be due to the models learning to focus too heavily on specific areas.

Complementing the pulp proteome via sampling with a picosecond infrared laser (PIRL)

OBJECTIVES

The aim of this investigation was the detailed analysis of the human pulp proteome using the new picosecond infrared laser (PIRL)-based sampling technique, which is based on a completely different mechanism compared to mechanical sampling. Proteome analysis of healthy pulp can provide data to define changes in the proteome associated with dental disease.

MATERIAL AND METHODS

Immediately after extraction of the entire, undamaged tooth, 15 wisdom teeth were deep frozen in liquid nitrogen and preserved at -80°C. Teeth were crushed, and the excised frozen pulps were conditioned for further analysis. The pulps were sampled using PIRL, and the aspirates digested with trypsin and analyzed with mass spectrometry. Pulp proteins were categorized according to their gene ontology terminus. Proteins identified exclusively in this study were searched in the Human Protein Atlas (HPA) for gaining information about the main known localization and function.

RESULTS

A total of 1348 proteins were identified in this study. The comparison with prior studies showed a match of 72%. Twenty-eight percent of the proteins were identified exclusively in this study. Considering HPA, almost half of these proteins were assigned to tissues that could be pulp specific.

CONCLUSION

PIRL is releasing proteins from the dental pulp which are not dissolved by conventional sampling techniques. Clinical Relevance The presented data extend current knowledge on dental pulp proteomics in healthy teeth and can serve as a reference for studies on pulp proteomics in dental disease.

Mass Spectrometric Lipid Profiles of Picosecond Infrared Laser-Generated Tissue Aerosols Discriminate Different Brain Tissues

BACKGROUND AND OBJECTIVES

A picosecond infrared laser (PIRL) has recently been demonstrated to cut biological tissue without scar formation based on the minimal destructive action on the surrounding cells. During cutting with PIRL, the irradiated tissue is ablated by a cold vaporization process termed desorption by impulsive vibrational excitation. In the resulting aerosol, all molecules are dissolved in small droplets and even labile biomolecules like proteins remain intact after ablation. It is hypothesized that these properties enable the PIRL in combination with mass spectrometry as an intelligent laser scalpel for guided surgery. In this study, it was tested if PIRL-generated tissue aerosols are applicable for direct analysis with mass spectrometry, and if the acquired mass spectra can be used to discriminate different brain areas.

MATERIALS AND METHODS

Brain tissues were irradiated with PIRL. The aerosols were collected and directly infused into a mass spectrometer via electrospray ionization without any sample preparation or lipid extraction.

RESULTS

The laser produced clear cuts with no marks of burning. Lipids from five different classes were identified in the mass spectra of all samples. By principal component analysis the different brain areas were clearly distinguishable from each other.

CONCLUSIONS

The results demonstrate the potential for real-time analysis of lipids with a PIRL-based laser scalpel, coupled to a mass spectrometer, for the discrimination of tissues during surgeries. Lasers Surg. Med. © 2019 Wiley Periodicals, Inc.

Mass spectrometry-based intraoperative tumor diagnostics

In surgical oncology, decisions regarding the amount of tissue to be removed can have important consequences: the decision between preserving sufficient healthy tissue and eliminating all tumor cells is one to be made intraoperatively. This review discusses the latest technical innovations for a more accurate tumor margin localization based on mass spectrometry. Highlighting the latest mass spectrometric inventions, real-time diagnosis seems to be within reach; focusing on the intelligent knife, desorption electrospray ionization, picosecond infrared laser and MasSpec pen, the current technical status is evaluated critically concerning its scientific and medical practice.