Improving Segmentation and Detection of Lesions in CT Scans Using Intensity Distribution Supervision.

We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks. We first build an intensity-based lesion probability (ILP) function from an intensity histogram of the target lesion. It is used to compute the probability of being the lesion for each voxel based on its intensity.

Improving segmentation and detection of lesions in CT scans using intensity distribution supervision.

We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks. We first build an intensity-based lesion probability (ILP) function from an intensity histogram of the target lesion. It is used to compute the probability of being the lesion for each voxel based on its intensity.

Deep Reinforcement Learning for Small Bowel Path Tracking using Different Types of Annotations.

Small bowel path tracking is a challenging problem considering its many folds and contact along its course. For the same reason, it is very costly to achieve the ground-truth (GT) path of the small bowel in 3D. In this work, we propose to train a deep reinforcement learning tracker using datasets with different types of annotations.

Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor.

Finding small lesions is very challenging due to lack of noticeable features, severe class imbalance, as well as the size itself. One approach to improve small lesion segmentation is to reduce the region of interest and inspect it at a higher sensitivity rather than performing it for the entire region. It is usually implemented as sequential or joint segmentation of organ and lesion, which requires additional supervision on organ segmentation.

Fully-automated detection of small bowel carcinoid tumors in CT scans using deep learning.

Background: Small bowel carcinoid tumor is a rare neoplasm and increasing in incidence. Patients with small bowel carcinoid tumors often experience long delays in diagnosis due to the vague symptoms, slow growth of tumors, and lack of clinician awareness. Computed tomography (CT) is the most common imaging study for diagnosis of small bowel carcinoid tumor.

Flow Field-Flow Fractionation with a Thickness-Tapered Channel.

This study introduces the thickness-tapered channel design for flow field-flow fractionation (FlFFF) for the first time. In this design, the channel thickness linearly decreases along the channel axis such that the flow velocity increases down the channel. Channel thickness is an important variable for controlling retention time and resolution in field-flow fractionation.

The Effects of Persimmon ( L.f.) Oligosaccharides on Features of the Metabolic Syndrome in Zebrafish.

Metabolic syndrome has become a global health care problem since it is rapidly increasing worldwide. The search for alternative natural supplements may have potential benefits for obesity and diabetes patients. fruit extract and its oligosaccharides, including gentiobiose, melibiose, and raffinose, were examined for their anti-insulin resistance and obesity-preventing effect in zebrafish larvae.

Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation.

We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement. To make the domain adaptation more controllable, we disentangle intensity and non-intensity features within a unique two-stream auto-encoding architecture, and selectively adapt the non-intensity features that are believed to be more transferable across domains. The segmentation prediction is performed by aggregating the disentangled features.

GRAPH-BASED SMALL BOWEL PATH TRACKING WITH CYLINDRICAL CONSTRAINTS.

We present a new graph-based method for small bowel path tracking based on cylindrical constraints. A distinctive characteristic of the small bowel compared to other organs is the contact between parts of itself along its course, which makes the path tracking difficult together with the indistinct appearance of the wall. It causes the tracked path to easily cross over the walls when relying on low-level features like the wall detection.

A Graph-theoretic Algorithm for Small Bowel Path Tracking in CT Scans.

We present a novel graph-theoretic method for small bowel path tracking. It is formulated as finding the minimum cost path between given start and end nodes on a graph that is constructed based on the bowel wall detection. We observed that a trivial solution with many short-cuts is easily made even with the wall detection, where the tracked path penetrates indistinct walls around the contact between different parts of the small bowel.

Lomens-P0 (mixed extracts of and ) regulate the expression of factors affecting premenstrual syndrome symptoms.

Background/objectives: Premenstrual syndrome (PMS) is a disorder characterized by repeated emotional, behavioral, and physical symptoms before menstruation, and the exact cause and mechanism are uncertain. Hyperprolactinemia interferes with the normal production of estrogen and progesterone, leading to PMS symptoms. Thus, we judged that the inhibition of prolactin hypersecretion could mitigate PMS symptoms.

Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning.

Objectives: To develop a convolutional neural network system to jointly segment and classify a hepatic lesion selected by user clicks in ultrasound images.

Methods: In total, 4309 anonymized ultrasound images of 3873 patients with hepatic cyst (n = 1214), hemangioma (n = 1220), metastasis (n = 1001), or hepatocellular carcinoma (HCC) (n = 874) were collected and annotated. The images were divided into 3909 training and 400 test images.

Evaluation of genetic toxicity, acute and sub-chronic oral toxicity and systemic safety of and 50% ethanolic extract mixture (APRG64) in vitro and in vivo (rodent and non-rodent animal models).

(AP) and (RG) are traditional medicinal plants. The bioflavonoid composition standardized by HPLC analysis was named APRG64. Despite many studies reported to beneficial bioactivities of AP and RG, very limited range of toxicity tests have documented.

Deep Small Bowel Segmentation with Cylindrical Topological Constraints.

We present a novel method for small bowel segmentation where a cylindrical topological constraint based on persistent homology is applied. To address the touching issue which could break the applied constraint, we propose to augment a network with an additional branch to predict an inner cylinder of the small bowel. Since the inner cylinder is free of the touching issue, a cylindrical shape constraint applied on this augmented branch guides the network to generate a topologically correct segmentation.

Deep vessel segmentation by learning graphical connectivity.

We propose a novel deep learning based system for vessel segmentation. Existing methods using CNNs have mostly relied on local appearances learned on the regular image grid, without consideration of the graphical structure of vessel shape. Effective use of the strong relationship that exists between vessel neighborhoods can help improve the vessel segmentation accuracy.

Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images.

We propose a framework for localization and classification of masses in breast ultrasound images. We have experimentally found that training convolutional neural network-based mass detectors with large, weakly annotated datasets presents a non-trivial problem, while overfitting may occur with those trained with small, strongly annotated datasets. To overcome these problems, we use a weakly annotated dataset together with a smaller strongly annotated dataset in a hybrid manner.

A Novel Cascade Classifier for Automatic Microcalcification Detection.

In this paper, we present a novel cascaded classification framework for automatic detection of individual and clusters of microcalcifications (μC). Our framework comprises three classification stages: i) a random forest (RF) classifier for simple features capturing the second order local structure of individual μCs, where non-μC pixels in the target mammogram are efficiently eliminated; ii) a more complex discriminative restricted Boltzmann machine (DRBM) classifier for μC candidates determined in the RF stage, which automatically learns the detailed morphology of μC appearances for improved discriminative power; and iii) a detector to detect clusters of μCs from the individual μC detection results, using two different criteria. From the two-stage RF-DRBM classifier, we are able to distinguish μCs using explicitly computed features, as well as learn implicit features that are able to further discriminate between confusing cases.

MouseNet v2: a database of gene networks for studying the laboratory mouse and eight other model vertebrates.

Laboratory mouse, Mus musculus, is one of the most important animal tools in biomedical research. Functional characterization of the mouse genes, hence, has been a long-standing goal in mammalian and human genetics. Although large-scale knockout phenotyping is under progress by international collaborative efforts, a large portion of mouse genome is still poorly characterized for cellular functions and associations with disease phenotypes.

Anesthetic management of an 8-month-old infant with osteogenesis imperfecta undergoing liver transplantation: a case report.

Anesthetic management of pediatric liver transplantation in a patient with osteogenesis imperfecta (OI) requires tough decisions and comprehensive considerations of the cascade of effects that may arise and the required monitoring. Total intravenous anesthesia (TIVA) with propofol and remifentanil was chosen as the main anesthetic strategy. Malignant hyperthermia (MH), skeletal fragility, anhepatic phase during liver transplantation, uncertainties of TIVA in children, and propofol infusion syndrome were considered and monitored.

Methyl p-Hydroxycinnamate Suppresses Lipopolysaccharide-Induced Inflammatory Responses through Akt Phosphorylation in RAW264.7 Cells.

Derivatives of caffeic acid have been reported to possess diverse pharmacological properties such as anti-inflammatory, anti-tumor, and neuroprotective effects. However, the biological activity of methyl p-hydroxycinnamate, an ester derivative of caffeic acid, has not been clearly demonstrated. This study aimed to elucidate the anti-inflammatory mechanism of methyl p-hydroxycinnamate in lipopolysaccharide (LPS)-stimulated RAW 264.

Aromadendrin Inhibits Lipopolysaccharide-Induced Nuclear Translocation of NF-κB and Phosphorylation of JNK in RAW 264.7 Macrophage Cells.

Aromadendrin, a flavonol, has been reported to possess a variety of pharmacological activities such as anti-inflammatory, antioxidant, and anti-diabetic properties. However, the underlying mechanism by which aromadendrin exerts its biological activity has not been extensively demonstrated. The objective of this study is to elucidate the anti-inflammatory mechanism of aromadedrin in lipopolysaccharide (LPS)-stimulated RAW 264.