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. Finally, the computed ILP map of each input CT scan is provided as additional supervision for network training, which aims to inform the network about possible lesion locations in terms of intensity values at no additional labeling cost. The method was applied to improve the segmentation of three different lesion types, namely, small bowel carcinoid tumor, kidney tumor, and lung nodule. The effectiveness of the proposed method on a detection task was also investigated. We observed improvements of 41.3% -> 47.8%, 74.2% -> 76.0%, and 26.4% -> 32.7% in segmenting small bowel carcinoid tumor, kidney tumor, and lung nodule, respectively, in terms of per case Dice scores. An improvement of 64.6% -> 75.5% was achieved in detecting kidney tumors in terms of average precision. The results of different usages of the ILP map and the effect of varied amount of training data are also presented.

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. Finally, the computed ILP map of each input CT scan is provided as additional supervision for network training, which aims to inform the network about possible lesion locations in terms of intensity values at no additional labeling cost. The method was applied to improve the segmentation of three different lesion types, namely, small bowel carcinoid tumor, kidney tumor, and lung nodule. The effectiveness of the proposed method on a detection task was also investigated. We observed improvements of 41.3% → 47.8%, 74.2% → 76.0%, and 26.4% → 32.7% in segmenting small bowel carcinoid tumor, kidney tumor, and lung nodule, respectively, in terms of per case Dice scores. An improvement of 64.6% → 75.5% was achieved in detecting kidney tumors in terms of average precision. The results of different usages of the ILP map and the effect of varied amount of training data are also presented.

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. Instead, we propose to utilize an intensity distribution of a target lesion at no additional labeling cost to effectively separate regions where the lesions are possibly located from the background. It is incorporated into network training as an auxiliary task. We applied the proposed method to segmentation of small bowel carcinoid tumors in CT scans. We observed improvements for all metrics (33.5% → 38.2%, 41.3% → 47.8%, 30.0% → 35.9% for the global, per case, and per tumor Dice scores, respectively.) compared to the baseline method, which proves the validity of our idea. Our method can be one option for explicitly incorporating intensity distribution information of a target in network training.

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. It is often used with positron emission tomography (PET) to capture anatomical and functional aspects of carcinoid tumors and thus to increase the sensitivity. PURPOSE: We compared three different kinds of methods for the automatic detection of small bowel carcinoid tumors on CT scans, which is the first to the best of our knowledge. METHODS: Thirty-three preoperative CT scans of 33 unique patients with surgically-proven carcinoid tumors within the small bowel were collected. Ground-truth segmentation of tumors was drawn on CT scans by referring to available 18 F-DOPA PET scans and the corresponding radiology report. These scans were split into the trainval set (n = 24) and the test positive set (n= 9). Additionally, 22 CT scans of 22 unique patients who had no evidence of the tumor were collected to comprise the test negative set. We compared three different kinds of detection methods, which are detection network, patch-based classification, and segmentation-based methods. We also investigated the usefulness of small bowel segmentation for reduction of false positives (FPs) for each method. Free-response receiver operating characteristic (FROC) curves and receiver operating characteristic (ROC) curves were used for lesion- and patient-level evaluations, respectively. Statistical analyses comparing the FROC and ROC curves were also performed. RESULTS: The detection network method performed the best among the compared methods. For lesion-level detection, the detection network method, without the small bowel segmentation-based filtering, achieved sensitivity values of (60.8%, 81.1%, 82.4%, 86.5%) at per-scan FP rates of (1, 2, 4 ,8), respectively. The use of the small bowel segmentation did not improve the performance ( p = 0.742 $p=0.742$ ). For patient-level detection, again the detection network method, but with the small bowel segmentation-based filtering, achieved the highest AUC of 0.86 with a sensitivity of 78% and specificity of 82% at the Youden point. CONCLUSIONS: The carcinoid tumors in this patient population were very small and potentially difficult to diagnose. The presented method showed reasonable sensitivity at small numbers of FPs for lesion-level detection. It also achieved a promising AUC for patient-level detection. The method may have clinical application in patients with this rare and difficult to detect disease.

Enhancement of Immune Functions by Limosilactobacillus reuteri KBL346: In Vitro and In Vivo Studies.

Lactobacilli have been widely used as probiotics because of their benefits for intestinal health and physiological functions. Among a variety of Lactobacillus genera, Limosilactobacillus reuteri has been studied for its ability to exert anti-inflammatory functions and its role in controlling metabolic disorders, as well as the production of the antimicrobial compound reuterin. However, the effects and mechanisms of L. reuteri on enhancing immune responses in the immunosuppressed states have been relatively understudied. In this study, we isolated an immunomodulatory strain, namely, L. reuteri KBL346 (KBL346), from a fecal sample of a 3-month-old infant in Korea. We evaluated the immunostimulatory activity and hematopoietic function of KBL346 in macrophages and cyclophosphamide (CPA)-induced immunosuppressed mice. KBL346 increased the phagocytic activity against Candida albicans MYA-4788 in macrophages, and as biomarkers for this, increased secretions of nitric oxide (NO) and prostaglandin E2 (PGE2) were confirmed. Also, the secretions of innate cytokines (TNF-α, IL-1ß, and IL-6) were increased. In CPA-induced immunosuppressed mice, KBL346 at a dosage of 1010 CFU/kg protected against spleen injury and suppressed levels of immune-associated parameters, including NK cell activity, T and B lymphocyte proliferation, CD4+ and CD8+ T cell abundance, cytokines, and immunoglobulins in vivo. The effects were comparable or superior to those in the Korean red ginseng positive control group. Furthermore, the safety assessment of KBL346 as a probiotic was conducted by evaluating its antibiotic resistance, hemolytic activity, cytotoxicity, and metabolic characteristics. This study demonstrated the efficacy and safety of KBL346, which could potentially be used as a supplement to enhance the immune system.

Lomens-P0 (mixed extracts of Hordeum vulgare and Chrysanthemum zawadskii) 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. MATERIALS/METHODS: Hordeum vulgare L. extract (HVE), Chrysanthemum zawadskii var. latilobum extract (CZE), and Lomens-P0 the mixture of these extracts were tested in subsequent experiments. The effect of extracts on prolactin secretion at the in vitro level was measured in GH3 cells. Nitric oxide and pro-inflammatory mediator expression were measured in RAW 264.7 cells to confirm the anti-inflammatory effect. Also, the hyperprolactinemic Institute for Cancer Research (ICR) mice model was used to measure extract effects on prolactin and hormone secretion and uterine inflammation. RESULTS: Anti-inflammatory effects of and prolactin secretion suppress by HVE and CZE were confirmed through in vitro experiments (P < 0.05). Treatment with Lomens-P0 inhibited prolactin secretion (P < 0.05) and restored normal sex hormone secretion in the hyperprolactinemia mice model. In addition, extracts significantly inhibited the expression of pro-inflammatory biomarkers, including interleukin-1ß, and -6, tumor necrosis factor-α, inducible nitric oxide synthase, and cyclooxygenase-2 (P < 0.01). We used high-performance liquid chromatography analyses to identify tricin and chlorogenic acid as the respective components of HVE and CZE that inhibit prolactin secretion. The Lomens-P0, which includes tricin and chlorogenic acid, is expected to be effective in improving PMS symptoms in the human body. CONCLUSIONS: The Lomens-P0 suppressed the prolactin secretion in hyperprolactinemia mice, normalized the sex hormone imbalance, and significantly suppressed the expression of inflammatory markers in uterine tissue. This study suggests that Lomens-P0 may have the potential to prevent or remedy materials to 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. Our network is composed of one shared encoder and two inference branches used for segmentation and classification and takes the concatenation of an input image and two Euclidean distance maps of foreground and background clicks provided by a user as input. The performance of hepatic lesion segmentation was evaluated based on the Jaccard index (JI), and the performance of classification was based on accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). RESULTS: We achieved performance improvements by jointly conducting segmentation and classification. In the segmentation only system, the mean JI was 68.5%. In the classification only system, the accuracy of classifying four types of hepatic lesions was 79.8%. The mean JI and classification accuracy were 68.5% and 82.2%, respectively, for the proposed joint system. The optimal sensitivity and specificity and the AUROC of classifying benign and malignant hepatic lesions of the joint system were 95.0%, 86.0%, and 0.970, respectively. The respective sensitivity, specificity, and the AUROC for classifying four hepatic lesions of the joint system were 86.7%, 89.7%, and 0.947. CONCLUSIONS: The proposed joint system exhibited fair performance compared to segmentation only and classification only systems. KEY POINTS: • The joint segmentation and classification system using deep learning accurately segmented and classified hepatic lesions selected by user clicks in US examination. • The joint segmentation and classification system for hepatic lesions in US images exhibited higher performance than segmentation only and classification only systems. • The joint segmentation and classification system could assist radiologists with minimal experience in US imaging by characterizing hepatic lesions.

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. Experimental evaluation is conducted on the original Mammographic Image Analysis Society (MIAS) and mini-MIAS databases, as well as our own Seoul National University Bundang Hospital digital mammographic database. It is shown that the proposed method outperforms comparable methods in terms of receiver operating characteristic (ROC) and precision-recall curves for detection of individual µCs and free-response receiver operating characteristic (FROC) curve for detection of clustered µCs.

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. There were no adverse events during the operation. Despite meticulous precautions with regard to the risk of MH, there was an episode of high fever (40℃) in the ICU a few hours after the operation, which was initially feared as MH. Fortunately, MH was ruled out as the fever subsided soon after hydration and antipyretics were given. Although the delivery of supportive care and the administration of dantrolene are the core principles in the management of MH, perioperative fever does not always mean a MH in patients at risk for MH, and other common causes of fever should also be considered.

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 264.7 macrophage cells. Methyl p-hydroxycinnamate significantly inhibited LPS-induced excessive production of pro-inflammatory mediators such as nitric oxide (NO) and PGE2 and the protein expression of iNOS and COX-2. Methyl p-hydroxycinnamate also suppressed LPS-induced overproduction of pro-inflammatory cytokines such as IL-1ß and TNF-α. In addition, methyl p-hydroxycinnamate significantly suppressed LPS-induced degradation of IκB, which retains NF-κB in the cytoplasm, consequently inhibiting the transcription of pro-inflammatory genes by NF-κB in the nucleus. Methyl p-hydroxycinnamate exhibited significantly increased Akt phosphorylation in a concentration-dependent manner. Furthermore, inhibition of Akt signaling pathway with wortmaninn abolished methyl p-hydroxycinnamate-induced Akt phosphorylation. Taken together, the present study clearly demonstrates that methyl p-hydroxycinnamate exhibits anti-inflammatory activity through the activation of Akt signaling pathway in LPS-stimulated RAW264.7 macrophage cells.