A deep learning-based segmentation pipeline for profiling cellular morphodynamics using multiple types of live cell microscopy.

MOTIVATION: Quantitative studies of cellular morphodynamics rely on extracting leading-edge velocity time series based on accurate cell segmentation from live cell imaging. However, live cell imaging has numerous challenging issues regarding accurate edge localization. Fluorescence live cell imaging produces noisy and low-contrast images due to phototoxicity and photobleaching. While phase contrast microscopy is gentle to live cells, it suffers from the halo and shade-off artifacts that cannot be handled by conventional segmentation algorithms. Here, we present a deep learning-based pipeline, termed MARS-Net (Multiple-microscopy-type-based Accurate and Robust Segmentation Network), that utilizes transfer learning and data from multiple types of microscopy to localize cell edges with high accuracy, allowing quantitative profiling of cellular morphodynamics. SUMMARY: To accurately segment cell edges and quantify cellular morphodynamics from live-cell imaging data, we developed a deep learning-based pipeline termed MARS-Net (multiple-microscopy-type-based accurate and robust segmentation network). MARS-Net utilizes transfer learning and data from multiple types of microscopy to localize cell edges with high accuracy. For effective training on distinct types of live-cell microscopy, MARS-Net comprises a pretrained VGG19 encoder with U-Net decoder and dropout layers. We trained MARS-Net on movies from phase-contrast, spinning-disk confocal, and total internal reflection fluorescence microscopes. MARS-Net produced more accurate edge localization than the neural network models trained with single-microscopy-type datasets. We expect that MARS-Net can accelerate the studies of cellular morphodynamics by providing accurate pixel-level segmentation of complex live-cell datasets.

Exacerbation of Multiple Sclerosis by BRAF/MEK Treatment for Malignant Melanoma: The Central Vein Sign to Distinguish Demyelinating Lesions From Metastases.

The emergence of immunomodulators as effective cancer treatments has been an important advance in cancer therapy. The combination therapy of BRAF/MEK inhibition with or without anti-CTLA-4 treatment causes an immunostimulatory effect that has greatly reduced death from melanoma. In this article, we present the case of a patient with prior multiple sclerosis (MS) and who later developed metastatic malignant melanoma, had a marked increase of magnetic resonance imaging (MRI) findings after treatment with the combination of trametinib (MEK) and dabrafenib (BRAF), diagnostic question of metastatic disease versus new MS lesions without brain biopsy is discussed. A healthy 49-year-old man was diagnosed with MS in October 2012. He was stable with an oral disease modifying drug until March of 2016 when the patient discovered a lump in his right groin. Biopsy was positive for S100 and BRAF V600 mutation. Combination MEK/BRAF was given and after immunotherapy an MRI showed 25 new gadolinium-enhancing lesions thought to be metastases. A brain biopsy was recommended but neurology and neuroimaging consultation showed that the MRI was consistent with demyelination (oval/ovoid, homogeneous and open-ring enhancement, and predominance of the central vein sign within lesions) rather than metastasis. Treatment for MS has been successful and there has been no return of his melanoma in 4 years. New immunotherapies are lifesaving but the modulation of the immune system can cause unpredictable events such are markedly increased MS activity. The awareness of the diagnostic value of the central vein sign provided a better outcome for this patient and could be a model in the future for others.