Brain Tumor Segmentation through Level Based Learning Model

Brain tumors are potentially fatal presence of cancer cells over a human brain, and they need to be segmented for accurate and reliable planning of diagnosis. Segmentation process must be carried out in different regions based on which the stages of cancer can be accurately derived. Glioma patients exhibit a different level of challenge in terms of cancer or tumors detection as the Magnetic Resonance Imaging (MRI) images possess varying sizes, shapes, positions, and modalities. The scanner used for sensing the location of tumors cells will be subjected to additional protocols and measures for accuracy, in turn, increasing the time and affecting the performance of the entire model. In this view, Convolutional Neural Networks deliver suitable models for efficient segmentation and thus delivered promising results. The previous strategies and models failed to adhere to diversity of sizes and shapes, proving to be a well-established solution for detecting tumors of bigger size. Tumors tend to be smaller in size and shape during their premature stages and they can easily evade the algorithms of Convolutional Neural Network (CNN). This proposal intends to furnish a detailed model for sensing early stages of cancer and hence perform segmentation irrespective of the current size and shape of tumors. The size of networks and layers will lead to a significant weightage when multiple kernel sizes are involved, especially in multi-resolution environments. On the other hand, the proposed model is designed with a novel approach including a dilated convolution and level-based learning strategy. When the convolution process is dilated, the process of feature extraction deals with multiscale objective and level-based learning eliminates the shortcoming of previous models, thereby enhancing the quality of smaller tumors cells and shapes. The level-based learning approach also encapsulates the feature reconstruction processes which highlights the sensing of small-scale tumors growth. Inclusively, segmenting the images is performed with better accuracy and hence detection becomes better when compared to that of hierarchical approaches.     

棕刚玉冶炼炉底硅铁瘤谨慎爆破清除

在一座棕刚玉冶炼炉底有一个硅铁瘤，必须进行爆破清除。对这块极其坚硬的硬骨头，克服钻孔困难、计算药量和实际药量偏差大等不利因素，采取有关措施，在顺利爆破时又保护了炉底、炉壁的石墨内衬，取得了较好的效果。     

Evaluation of two Compton camera models for scintimammography

We study the performance of a Si/LaBr₃:Ce Compton camera model for scintimammography, and compare it with a Si/NaI(Tl) model of similar geometry. The GEANT4 simulation toolkit was used to study the behaviour of the cameras at 511 keV. Certain simulation steps, such as the modelling of radionuclide decay times, scintillation photon transport and interactions with photomultipliers, as well as detector dead time corrections were included to make the modelling of the cameras more realistic than previous studies. The Si/LaBr₃:Ce Compton camera shows superior efficiency of 2.0×10⁻³ and resolution of 5.3 mm over the Si/NaI(Tl) Compton camera model which has the efficiency of 1.6×10⁻³ and resolution of 6.9 mm at a source-to-scatterer distance of interest, 2.5 cm. A similar result sequence is obtained for two breast tumours of 5 mm diameter embedded in the medial region of an average-size breast phantom of thickness 5 cm. Notably, the signal-to-noise ratios (SNR) obtained for the Si/LaBr₃:Ce camera are 9.7 and 3.4 for tumour/background radiation uptakes of 10:1 and 6:1, whereas 6.8 and 2.4 were obtained for the Si/NaI(Tl) camera model for the same tumour/background radiation uptakes respectively. It is therefore envisioned that with lower cost, LaBr₃:Ce could replace NaI(Tl) as the Compton camera absorber.     

Detection of brain tumour from MRI image using modified region growing and neural network

Abstract

Region growing is an important application of image segmentation in medical research for detection of tumour. In this paper, we propose an effective modified region growing technique for detection of brain tumour. It consists of four steps which includes: (i) pre-processing; (2) modified region growing by the inclusion of an additional orientation constraint in addition to the normal intensity constrain; (3) feature extraction of the region; and (4) final classification using the neural network. The performance of the proposed technique is systematically evaluated using the magnetic resonance imaging (MRI) brain images received from the public sources. For validating the effectiveness of the modified region growing, we have considered the quantity rate parameter. For the evaluation of the proposed technique of tumour detection, we make use of sensitivity, specificity and accuracy values which we compute from finding out false positive, false negative, true positive and true negative. Comparative analyses were made of the normal and the modified region growing using both the Feed Forward Neural Network (FFNN) and Radial Basis Function (RBF) neural network. From the results obtained, we could see that the proposed technique achieved the accuracy of 80% for the testing dataset, which clearly demonstrated the effectiveness of the modified region growing when compared to the normal technique.     

用文王八卦分析乳房疾病热图

笔者利用AGA红外热象仪检查了200多例妇女乳房疾病患者,经对乳房热图的统计分析,发现乳房肿瘤的常发区域在:外上象限45～50％,内上象限12～15％,乳头乳晕区15～20％。这一结论不仅符合乳房生理解剖学理论,而且与古老的文王八卦方位结构规律有着惊人的吻合之处。笔者根据文王八卦的宇宙、人体全息的概念,分析热图,并对50例病人进行了治疗的尝试,受益匪浅     

Tissue-Engineering the Fibrous Pancreatic Tumour Stroma Capsule in 3D Tumouroids to Demonstrate Paclitaxel Response

Pancreatic cancer is a unique cancer in that up to 90% of its tumour mass is composed of a hypovascular and fibrotic stroma. This makes it extremely difficult for chemotherapies to be delivered into the core of the cancer mass. We tissue-engineered a biomimetic 3D pancreatic cancer (“tumouroid”) model comprised of a central artificial cancer mass (ACM), containing MIA Paca-2 cells, surrounded by a fibrotic stromal compartment. This stromal compartment had a higher concentration of collagen type I, fibronectin, laminin, and hyaluronic acid (HA) than the ACM. The incorporation of HA was validated with alcian blue staining. Response to paclitaxel was determined in 2D MIA Paca-2 cell cultures, the ACMs alone, and in simple and complex tumouroids, in order to demonstrate drug sensitivity within pancreatic tumouroids of increasing complexity. The results showed that MIA Paca-2 cells grew into the complex stroma and invaded as cell clusters with a maximum distance of 363.7 µm by day 21. In terms of drug response, the IC₅₀ for paclitaxel for MIA Paca-2 cells increased from 0.819 nM in 2D to 3.02 nM in ACMs and to 5.87 nM and 3.803 nM in simple and complex tumouroids respectively, indicating that drug penetration may be significantly reduced in the latter. The results demonstrate the need for biomimetic models during initial drug testing and evaluation.     

3’-脱氧-3’-~(18)F-氟代胸腺嘧啶核苷的合成研究

采用TRACERlab FXF-N自动化合成装置制备了肿瘤显像剂3’-脱氧-3’-18F-氟代胸腺嘧啶核苷(18F-FLT)。以3-N-t-叔丁氧羰基-1-[5’-O-(4,4’-二甲氧基三苯甲基)-2’-脱氧-3’-O-(4-硝基苯磺酰基)-β-D-苏型-呋喃戊糖基]胸苷为前体,经过亲核氟化、盐酸水解两步反应及HPLC分离纯化制备18F-FLT,研究了不同因素对合成效率的影响。结果表明,总合成时间约55 min,未校正18F衰变情况下,放射化学产率约25%,放化纯度大于95%。     

A model for glioma cell migration on collagen and astrocytes

We present a model for the migration of glioma cells on substrates of collagen and astrocytes. The model is based on a cellular automaton where the various dynamical effects are introduced through adequate evolution rules. Using our model, we investigate the role of homotype and heterotype gap junction communication and show that it is possible to reproduce the corresponding experimental migration patterns. In particular, we confirm the experimental findings that inhibition of homotype gap junctions favours migration while heterotype inhibition hinders it. Moreover, the effect of heterotype gap junction inhibition dominates that of homotype inhibition.     

卟啉化合物在抗肿瘤方面的应用现状及进展

卟啉化合物的出现,在抗肿瘤方面起到了重要的作用,它可以在肿瘤前期对患者进行有效的治疗。天然的卟啉化合物,在实际的使用中,没有合成的卟啉化合物方便,加上现在的天然卟啉化合物已经不满足于现在的试验研究,所以现在也在大量的出现人工卟啉化合物。人工卟啉化合物在特性上和化学性质上和天然卟啉化合物相同,在药用方面也比天然卟啉化合物更好。     

双歧因子人工蜂粮保健食品及抑瘤作用研究

蜂花粉经过仿生发酵,酿制出人工蜂粮,再配伍双歧因子和蜂胶制成一种复合制品,并对此制品进行了抗肿瘤(S-180)方面的研究,抑瘤率达67.6%。