Spinal cord gray matter segmentation using deep dilated convolutions

Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and was also recently found relevant as a biomarker for disability in amyotrophic lateral sclerosis. The ability to automatically segment the GM is, therefore, an important task for modern studies of the spinal cord. In this work, we devise a modern, simple and end-to-end fully automated human spinal cord gray matter segmentation method using Deep Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate our method against six independently developed methods on a GM segmentation challenge and report state-of-the-art results in 8 out of 10 different evaluation metrics as well as major network parameter reduction when compared to the traditional medical imaging architectures such as U-Nets.Comment: 13 pages, 8 figure

Hydatid disease of the liver: thirty years of surgical experience.

Hydatid disease of the liver is a relatively frequent disease. Although the natural history is almost completely known, several complications may occur. The aim of this study was to show that radical surgical resection of the hepatic hydatid cyst is a safe and very effective technique, based on our results after 30-year experience. A review of most significant studies was carried out. We retrospectively evaluated our surgical cases. From January 1973 to December 2003 we treated 216 patients, 98 males and 118 females. Survival was compared with the Kaplan-Meier test, using log-rank analysis to compare data. Differences with a p value less than 0.05 were considered significant. A total of 279 cysts were excised. We performed pericystectomy in 122 cases, 73 of which closed. We also performed 19 atypical resections, 10 segmentectomies, 20 lobectomies and 2 percutaneous treatments. In more than 90% of cases, preoperative data collection was completed by preoperative ultrasound. The cumulative morbidity was 13%. The recurrence rate amounted to 4.3% at 5 years and 7% at 10 years: of these, 6 occurred after non-radical surgery and 2 after total pericystectomy or liver resection (p < 0.001). Technical advances and accumulated experience permit safe treatment of hepatic hydatid cysts by radical resection, with an almost zero recurrence rate, making it the treatment of choice over partial resection. The utility of percutaneous treatment remains confined to limited indications, such as laparoscopy

Characterization of the most frequent ATP7B mutation causing Wilson disease in hepatocytes from patient induced pluripotent stem cells

H1069Q substitution represents the most frequent mutation of the copper transporter ATP7B causing Wilson disease in Caucasian population. ATP7B localizes to the Golgi complex in hepatocytes but moves in response to copper overload to the endo-lysosomal compartment to support copper excretion via bile canaliculi. In heterologous or hepatoma-derived cell lines, overexpressed ATP7B-H1069Q is strongly retained in the ER and fails to move to the post-Golgi sites, resulting in toxic copper accumulation. However, this pathogenic mechanism has never been tested in patients' hepatocytes, while animal models recapitulating this form of WD are still lacking. To reach this goal, we have reprogrammed skin fibroblasts of homozygous ATP7B-H1069Q patients into induced pluripotent stem cells and differentiated them into hepatocyte-like cells. Surprisingly, in HLCs we found one third of ATP7B-H1069Q localized in the Golgi complex and able to move to the endo-lysosomal compartment upon copper stimulation. However, despite normal mRNA levels, the expression of the mutant protein was only 20% compared to the control because of endoplasmic reticulum-associated degradation. These results pinpoint rapid degradation as the major cause for loss of ATP7B function in H1069Q patients, and thus as the primary target for designing therapeutic strategies to rescue ATP7B-H1069Q function

A Mystery Unraveled: Non-tumorigenic pluripotent stem cells in human adult tissues

Embryonic stem cells and induced pluripotent stem cells have emerged as the gold standard of pluripotent stem cells and the class of 10 stem cell with the highest potential for contribution to regenerative and therapeutic application; however, their translational use is often impeded by teratoma formation, commonly associated with pluripotency. We discuss a population of nontumorigenic pluripotent stem cells, termed Multilineage Differentiating Stress Enduring (Muse) cells, which offer an innovative and 15 exciting avenue of exploration for the potential treatment of various human diseases. Areas covered: This review discusses the origin of Muse cells, describes in detail their various unique characteristics, and considers future avenues of their application and investigation with respect to what is currently known 20 of adult pluripotent stem cells in scientific literature. We begin by defining cell potency, then discussing both mesenchymal and various reported populations of pluripotent stem cells, and finally, delving into Muse cells and what sets them apart from their contemporaries. Expert opinion: Muse cells derived from adipose tissue (Muse-AT) are 25 efficiently, routinely and painlessly isolated from human lipoaspirate material, exhibit tripoblastic differentiation both spontaneously and under media-specific induction, and do not form teratomas. We describe qualities specific to Muse-ATcells and their potential impact on the field of regenerative medicine and cell therapy.Fil: Simerman, Ariel A.. University of California; Estados UnidosFil: Perone, Marcelo Javier. University of California; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires; ArgentinaFil: Gimeno, Maria Laura. University of California; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires; ArgentinaFil: Dumesic, Daniel A.. University of California; Estados UnidosFil: Chazenblak, Gregorio D.. University of California; Estados Unido

Unsupervised domain adaptation for medical imaging segmentation with self-ensembling

Recent advances in deep learning methods have come to define the state-of-the-art for many medical imaging applications, surpassing even human judgment in several tasks. Those models, however, when trained to reduce the empirical risk on a single domain, fail to generalize when applied to other domains, a very common scenario in medical imaging due to the variability of images and anatomical structures, even across the same imaging modality. In this work, we extend the method of unsupervised domain adaptation using self-ensembling for the semantic segmentation task and explore multiple facets of the method on a small and realistic publicly-available magnetic resonance (MRI) dataset. Through an extensive evaluation, we show that self-ensembling can indeed improve the generalization of the models even when using a small amount of unlabelled data.Comment: 15 pages, 9 figure