Deep Learning Based Brain Tumor Segmentation: A Survey

Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we use this survey to provide a comprehensive study of recently developed deep learning based brain tumor segmentation techniques. More than 100 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality processes. We also provide insightful discussions for future development directions

Quantitative assessment of Pulmonary Alveolar Proteinosis (PAP) with ultra-dose CT and correlation with Pulmonary Function Tests (PFTs)

<div><p>Background</p><p>The purpose of this study was to investigate whether ultra-low-dose chest computed tomography (CT) can be used for visual assessment of CT features in patients with pulmonary alveolar proteinosis (PAP) and to evaluate the relationship between the quantitative analysis of the ultra-low-dose CT scans and the pulmonary function tests (PFTs).</p><p>Methods</p><p>Thirty-eight patients (mean [SD] age, 44.47 [12.28] years; 29 males, 9 females) with PAP were enrolled and subjected to two scans each with low-dose CT (reference parameters: 120 kV and 50 mAs) and ultra-low-dose CT (reference parameters, 80 kV, 25 mAs). Images were reconstructed via filtered back projection (FBP) for low-dose CT and iterative reconstruction (IR) for ultra-low-dose CT. All patients underwent PFT. The Visual analysis for ground glass opacity (GGO) is performed. The quantitative CT and PFT results were analyzed by canonical correlations.</p><p>Results</p><p>The mean body mass index (BMI) was 25.37±3.26 kg/m². The effective radiation doses were 2.30±0.46 and 0.24±0.05 mSv for low-dose and ultra-low-dose CT, respectively. The size-specific dose estimates were 5.81±0.81 and 0.62±0.09 mSv for low-dose and ultra-low-dose CT. GGOs and interlobular septal thickening were observed bilaterally in all patients. The average visual GGO score was lower in the upper field (2.67±1.24) but higher in the middle and lower fields (3.08±1.32 and 3.08±0.97, respectively). The average score for the whole lung was 2.94±1.19. There is a significant correlation between PFTs and quantitative of ultra-low-dose CT (canonical loading = 0.78).</p><p>Conclusions</p><p>Ultra-low-dose CT has the potential to quantify the lung parenchyma changes of PAP. This technique could provide a sensitive and objective assessment of PAP and has good relation with PFTs. In addition, the radiation dose of ultra-low-dose CT was very low.</p></div

MiR542-3p Regulates the Epithelial-Mesenchymal Transition by Directly Targeting BMP7 in NRK52e

Accumulating evidence demonstrated that miRNAs are highly involved in kidney fibrosis and Epithelial-Eesenchymal Transition (EMT), however, the mechanisms of miRNAs in kidney fibrosis are poorly understood. In this work, we identified that miR542-3p could promote EMT through down-regulating bone morphogenetic protein 7 (BMP7) expression by targeting BMP7 3′UTR. Firstly, real-time PCR results showed that miR542-3p was significantly up-regulated in kidney fibrosis in vitro and in vivo. Moreover, Western blot results demonstrated that miR542-3p may promote EMT in the NRK52e cell line. In addition, we confirmed that BMP7, which played a crucial role in anti-kidney fibrosis and suppressed the progression of EMT, was a target of miR542-3p through Dual-Luciferase reporter assay, as did Western blot analysis. The effects of miR542-3p on regulating EMT could also be suppressed by transiently overexpressing BMP7 in NRK52e cells. Taken together, miR542-3p may be a critical mediator of the induction of EMT via directly targeting BMP7

Patient clinical characteristics and radiation dose.

<p>Patient clinical characteristics and radiation dose.</p

Diagnostic confidence in CT findings from patients with PAP in LDCT and ultra-low-dose CT.

<p>Diagnostic confidence in CT findings from patients with PAP in LDCT and ultra-low-dose CT.</p

Canonical loadings for input and output variates for patients with PAP.

<p>Canonical loadings for input and output variates for patients with PAP.</p

Quantitative assessments with low-dose and ultra-low-dose CT.

<p>Quantitative assessments with low-dose and ultra-low-dose CT.</p