A deep level set method for image segmentation

This paper proposes a novel image segmentation approachthat integrates fully convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the integrated method can incorporatesmoothing and prior information to achieve an accurate segmentation.Furthermore, different than using the level set model as a post-processingtool, we integrate it into the training phase to fine-tune the FCN. Thisallows the use of unlabeled data during training in a semi-supervisedsetting. Using two types of medical imaging data (liver CT and left ven-tricle MRI data), we show that the integrated method achieves goodperformance even when little training data is available, outperformingthe FCN or the level set model alone

Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation

In cardiac magnetic resonance imaging, fully-automatic segmentation of the heart enables precise structural and functional measurements to be taken, e.g. from short-axis MR images of the left-ventricle. In this work we propose a recurrent fully-convolutional network (RFCN) that learns image representations from the full stack of 2D slices and has the ability to leverage inter-slice spatial dependences through internal memory units. RFCN combines anatomical detection and segmentation into a single architecture that is trained end-to-end thus significantly reducing computational time, simplifying the segmentation pipeline, and potentially enabling real-time applications. We report on an investigation of RFCN using two datasets, including the publicly available MICCAI 2009 Challenge dataset. Comparisons have been carried out between fully convolutional networks and deep restricted Boltzmann machines, including a recurrent version that leverages inter-slice spatial correlation. Our studies suggest that RFCN produces state-of-the-art results and can substantially improve the delineation of contours near the apex of the heart.Comment: MICCAI Workshop RAMBO 201

The Deep Poincare Map: A Novel Approach for Left Ventricle Segmentation

Precise segmentation of the left ventricle (LV) within cardiac MRI images is a prerequisite for the quantitative measurement of heart function. However, this task is challenging due to the limited availability of labeled data and motion artifacts from cardiac imaging. In this work, we present an iterative segmentation algorithm for LV delineation. By coupling deep learning with a novel dynamic-based labeling scheme, we present a new methodology where a policy model is learned to guide an agent to travel over the image, tracing out a boundary of the ROI – using the magnitude difference of the Poincaré map as a stopping criterion. Our method is evaluated on two datasets, namely the Sunnybrook Cardiac Dataset (SCD) and data from the STACOM 2011 LV segmentation challenge. Our method outperforms the previous research over many metrics. In order to demonstrate the transferability of our method we present encouraging results over the STACOM 2011 data, when using a model trained on the SCD dataset

Straightforward Procedure for Laboratory Production of DNA Ladder

DNA ladder is commonly used to determine the size of DNA fragments by electrophoresis in routine molecular biology laboratories. In this study, we report a new procedure to prepare a DNA ladder that consists of 10 fragments from 100 to 1000 bp. This protocol is a combination of routinely employed methods: cloning, PCR, and partial digestion with restriction enzymes. DNA fragments of 100 bp with unique restriction site at both ends were self-ligated to create a tandem repeat. Once being cloned, the tandem repeat was rapidly amplified by PCR and partially digested by restriction enzymes to produce a ladder containing multimers of the repeated DNA fragments. Our procedure for production of DNA ladder could be simple, time saving, and inexpensive in comparison with current ones widely used in most laboratories

Geology, Pb and S Isotope Geochemistry, and Genesis of the Na Bop-Pu Sap Lead-Zinc Deposit in the Cho Don area, Northeastern Vietnam

The Na Bop-Pu Sap Pb-Zn ore bodies represent a typical vein-type lead-zinc deposit situated in the Cho Don area and are currently being extracted for their lead and zinc resources. This deposit is characterized by its significant scale and quality and is considered one of the prominent lead-zinc deposits in the Cho Don area. Despite its significance, this deposit has not received adequate attention, resulting in limited knowledge of its geology, mineralization, and deposit genesis model. To address this knowledge gap, our study utilized several methodologies, including field surveying, ore mineral analysis under a microscope, and S and Pb isotopic geochemistry. By employing these approaches, we were able to obtain specific insights into the origin of mineralization and the deposit model. Our field survey suggests that the ore deposits are formed as Pb-Zn-bearing veins along Devonian shale, claystone, and limestone faults. Microscopic analyses of the veins reveal the presence of galena, sphalerite, chalcopyrite, pyrite, arsenopyrite, and pyrrhotite as ore minerals, and quartz, calcite, dolomite, and chalcedony as gangue minerals. Sulfur-isotope values (δ34SCDT) of galena 5.3 to 0.1‰ (average 2.8‰), sphalerite 6.8 to 2.5‰ (average 5.3‰), and pyrite 5.8 to 4.1‰ (average 4.9‰) indicate that the sulfide mineralization may be related to a deep source, possibly originating from magmatic activity in the region and contaminated by carbonate-bearing marine sedimentary rocks. Lead-isotope studies indicate a model age of 598-424 Ma for the lead reservoir, consistent with the possible presence of local source rocks containing sulfur. The lead and sulfur in the ore veins were probably contaminated by Devonian carbonate-bearing marine sedimentary rocks and leached from Neoproterozoic to Cambrian magmatic activity. The lead-zinc deposits in Na Bop-Pu Sap do not display any Mississippi valley-type (MVT) or Sedimentary exhalative (SEDEX) lead-zinc deposit characteristics, as they appear to be related to shear zone-hosted lead-zinc deposits

Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs

Cardiac left ventricle (LV) quantification provides a tool for diagnosing cardiac diseases. Automatic calculation of all relevant LV indices from cardiac MR images is an intricate task due to large variations among patients and deformation during the cardiac cycle. Typical methods are based on segmentation of the myocardium or direct regression from MR images. To consider cardiac motion and deformation, recurrent neural networks and spatio-temporal convolutional neural networks (CNNs) have been proposed. We study an approach combining state-of-the-art models and emphasizing transfer learning to account for the small dataset provided for the LVQuan19 challenge. We compare 2D spatial and 3D spatio-temporal CNNs for LV indices regression and cardiac phase classification. To incorporate segmentation information, we propose an architecture-independent segmentation-based regularization. To improve the robustness further, we employ a search scheme that identifies the optimal ensemble from a set of architecture variants. Evaluating on the LVQuan19 Challenge training dataset with 5-fold cross-validation, we achieve mean absolute errors of 111 +- 76mm^2, 1.84 +- 0.9mm and 1.22 +- 0.6mm for area, dimension and regional wall thickness regression, respectively. The error rate for cardiac phase classification is 6.7%.Comment: Accepted at the MICCAI Workshop STACOM 201

Serum interleukin 6 concentration in patients with pemphigus

Pemphigus is a rare autoimmune blistering disease that detrimentally affects the integumentary system and decreases patients’ quality of life. Recent studies have shown that interleukin 6 (IL-6) is closedly involved in the immunophathogenesis of pemphigus. Therefore, this study was performed to evaluate the role of IL-6 in the pathogenesis and severity of pemphigus disease. The case-series study was conducted in Ho Chi Minh City Hospital of Dermato-Venereology from January 2022 to August 2022, involving 26 patients with pemphigus vulgaris (PV), 4 patients with pemphigus foliaceus (PF), and 20 healthy volunteers. The serum IL-6 concentrations of patients with PV and PF were significantly higher than those of the healthy volunteers (P < 0.001). Serum IL-6 concentrations were significantly higher in patients with a positive than a negative Nikolsky sign (P < 0.001). A significant correlation was found between the serum IL-6 concentration and the pemphigus disease area index (r = 0.8, P < 0.001). Our results suggest that IL-6 may play an important role in the pathogenesis and severity of pemphigus. Therefore, new therapies targeting IL-6 may be a promising choice for treating pemphigus, especially in its severe forms

Epidemiology of forest malaria in central Vietnam: a large scale cross-sectional survey

In Vietnam, a large proportion of all malaria cases and deaths occurs in the central mountainous and forested part of the country. Indeed, forest malaria, despite intensive control activities, is still a major problem which raises several questions about its dynamics. A large-scale malaria morbidity survey to measure malaria endemicity and identify important risk factors was carried out in 43 villages situated in a forested area of Ninh Thuan province, south central Vietnam. Four thousand three hundred and six randomly selected individuals, aged 10–60 years, participated in the survey. Rag Lays (86%), traditionally living in the forest and practising "slash and burn" cultivation represented the most common ethnic group. The overall parasite rate was 13.3% (range [0–42.3] while Plasmodium falciparum seroprevalence was 25.5% (range [2.1–75.6]). Mapping of these two variables showed a patchy distribution, suggesting that risk factors other than remoteness and forest proximity modulated the human-vector interactions. This was confirmed by the results of the multivariate-adjusted analysis, showing that forest work was a significant risk factor for malaria infection, further increased by staying in the forest overnight (OR= 2.86; 95%CI [1.62; 5.07]). Rag Lays had a higher risk of malaria infection, which inversely related to education level and socio-economic status. Women were less at risk than men (OR = 0.71; 95%CI [0.59; 0.86]), a possible consequence of different behaviour. This study confirms that malaria endemicity is still relatively high in this area and that the dynamics of transmission is constantly modulated by the behaviour of both humans and vectors. A well-targeted intervention reducing the "vector/forest worker" interaction, based on long-lasting insecticidal material, could be appropriate in this environment

Determinants of postnatal spleen tissue regeneration and organogenesis

Abstract The spleen is an organ that filters the blood and is responsible for generating blood-borne immune responses. It is also an organ with a remarkable capacity to regenerate. Techniques for splenic auto-transplantation have emerged to take advantage of this characteristic and rebuild spleen tissue in individuals undergoing splenectomy. While this procedure has been performed for decades, the underlying mechanisms controlling spleen regeneration have remained elusive. Insights into secondary lymphoid organogenesis and the roles of stromal organiser cells and lymphotoxin signalling in lymph node development have helped reveal similar requirements for spleen regeneration. These factors are now considered in the regulation of embryonic and postnatal spleen formation, and in the establishment of mature white pulp and marginal zone compartments which are essential for spleen-mediated immunity. A greater understanding of the cellular and molecular mechanisms which control spleen development will assist in the design of more precise and efficient tissue grafting methods for spleen regeneration on demand. Regeneration of organs which harbour functional white pulp tissue will also offer novel opportunities for effective immunotherapy against cancer as well as infectious diseases

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Background: Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Methods: Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). Results: By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images. On a short-axis image test set of 600 subjects, it achieves an average Dice metric of 0.94 for the LV cavity, 0.88 for the LV myocardium and 0.90 for the RV cavity. The mean absolute difference between automated measurement and manual measurement was 6.1 mL for LVEDV, 5.3 mL for LVESV, 6.9 gram for LVM, 8.5 mL for RVEDV and 7.2 mL for RVESV. On long-axis image test sets, the average Dice metric was 0.93 for the LA cavity (2-chamber view), 0.95 for the LA cavity (4-chamber view) and 0.96 for the RA cavity (4-chamber view). The performance is comparable to human inter-observer variability. Conclusions: We show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures