Auswahl der wissenschaftlichen Literatur zum Thema „Wb 115 h322 2018“

In Bangladesh groundnut shelling is done manually which is laborious, time consuming and costly. Shelling of groundnut pod with the help of mechanical power can be a probable solution of this problem. A power groundnut sheller was designed and fabricated in Farm Machinery and Postharvest Process Engineering (FMPE) Division, Bangladesh Agricultural Research Institute (BARI), Gazipur during 2011-13. The sheller was made of Mild Steel (MS) angle bar, MS flat bar, MS rod, MS sheet, MS sieve, rubber pad etc. The shelling capacities of power groundnut sheller were 110 and 115 kg/h for Dhaka-1 and BARI Badam-8, respectively. Average breakage of groundnut kernel was 2% at 7.5% moisture content (wb). The maximum and minimum unshelled pods were about12.4% and 9.18% for Dhaka -1 and BARI Badam-8, respectively. The shelling efficiency of the power groundnut sheller for Dhaka-1 and BARI Badam-8 were 86.6 and 88.82% respectively at 11.5% moisture content (wb). Winnowing efficiency was found to be 99% in the power groundnut sheller. The use of power groundnut sheller can reduce the cost of shelling by 76% over the manual groundnut sheller. This power groundnut sheller is recommended for shelling of groundnut at farm level and small industry level in Bangladesh.Bangladesh J. Agril. Res. 43(4): 631-645, December 2018

Sunflower production is increasing in Bangladesh, but farmers face problems separating the seeds from the sunflower heads. Sunflowers are traditionally threshed by beating the heads manually with a stick. The goal of this experiment is to design and develop a motor-driven machine that separates the seeds from the sunflower. An orthographic projection was drawn using SolidWorks 2016 software. The sunflower threshing machine was then fabricated according to the drawing in the FMPE Divisional workshop using locally available materials in 2017-18. The developed sunflower threshing machine was modified in 2018-19. The improved model was further modified to reduce the overall dimensions while maintaining the same capacity of the machine. The number of threshing rollers was reduced from 5 to 4. A threshing fan has been added to the improved version to separate the dust from the grains. The capacity of the motorized sunflower thresher was 115 and 304% higher than that of the pedal thresher and manual threshing, respectively. The capacity of the thresher was varied with moisture content. The capacity of the thresher varied from 89 to 125 kg/h at moisture content from 31 to 62% (wb).

Introduction: The main aim of many blood centers are to supply sufficient amount of safe blood and blood components whenever required. Shortage of blood may be due to low donation rate, inadequate storage, improper transportation and wastage at ward side. Wastage of blood can have a negative impact on blood transfusion services. Aim: To determine the rates of wastage of blood and blood components units at the ward side and identify the various reasons for wastage and explain the strategies to reduce the wastage rate. Materials and Methods: This retrospective study was conducted in the Department of Transfusion Medicine, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India from January 2018 to December 2020. The data was collected from the blood centre record. All blood and blood components issued during the study period were included in the study. The number, type and blood group of blood and blood components issued, the number of blood units wasted after issue, and the reason for wastage was collected. The collected data were entered in Microsoft excel and analysed using Statistical Package for the Social Science (SPSS) version 21.0. Descriptive statistics were performed as necessary. Results: During the study period, a total of 69198 units of blood and blood components were issued to different wards and operation theatres in the hospital. Among the total issues, 26863 (38.82%) were in the form of Packed Red Blood Cells (PRBC), 3968 (5.73%) in the form of Whole Blood (WB), 26069 (37.67%) in the form of Fresh Frozen Plasma (FFP), 11272 (16.29%) in the form of Random Donor Platelets (RDP), 933 (1.35%) in the form of Cryoprecipitate (CP) and 93 (0.13%) in the form of Single Donor Platelets (SDP). Among issued, 115 (0.17%) of blood and blood components were wasted with packed red cells accounting for 51 (44.35%). Among the various reasons, 43 (37.39%) was due to demise of patients before transfusion was initiated, followed by non requirement by the patients due to no loss/minimal loss blood during surgery 24 (20.87%). Majority of blood and blood components were wasted by the Department of Emergency Medicine (EMD) 36 (31.3%) followed by the Department of Neurology 20 (17.39%) and Orthopaedics 12 (10.43%). Conclusion: Implementation of proper blood transfusion policies and continuous educational programs in coordination with clinicians and staff nurses at ward side and operation theatres will help to decrease the blood wastage at ward side.

Liver segmentation is relevant for several clinical applications. Automatic liver segmentation using convolutional neural networks (CNNs) has been recently investigated. In this paper, we propose a new approach of combining a largest connected component (LCC) algorithm, as a post-processing step, with CNN approaches to improve liver segmentation accuracy. Specifically, in this study, the algorithm is combined with three well-known CNNs for liver segmentation: FCN-CRF, DRIU and V-net. We perform the experiment on a variety of liver CT images, ranging from non-contrast enhanced CT images to low-dose contrast enhanced CT images. The methods are evaluated using Dice score, Haudorff distance, mean surface distance, and false positive rate between the liver segmentation and the ground truth. The quantitative results demonstrate that the LCC algorithm statistically significantly improves results of the liver segmentation on non-contrast enhanced and low-dose images for all three CNNs. The combination with V-net shows the best performance in Dice score (higher than 90%), while the DRIU network achieves the smallest computation time (2 to 6 seconds) for a single segmentation on average. The source code of this study is publicly available at https://github.com/kennyha85/Liver-segmentation. Keywords: Liver segmentations, CNNs, Connected Components, Post processing Reference [1] K.A. McGlynn, J.L. Petrick, W.T. London, Global epidemiology of hepatocellular carcinoma: An emphasis on demographic and regional variability. Clinics in liver disease 19(2) (2015) 223-238. [2] M. Mohammadian, N. Mahdavifar, A. Mohammadian-Hafshejani, H. Salehiniya, Liver cancer in the world: epidemiology, incidence, mortality and risk factors, World Cancer Res J. 5(2) (2018) e1082. [3] T.T. Hong, N. Phuong Hoa, S.M. Walker, P.S. Hill, C. Rao, Completeness and reliability of mortality data in Viet Nam: Implications for the national routine health management information system, PloS one 13(1) 2018) e0190755. https://doi.org/10.1371/journal.pone.0190755. [4] T. Pham, L. Bui, G. Kim, D. Hoang, T. Tran, M. Hoang, Cancers in Vietnam-Burden and Control Efforts: A Narrative Scoping Review. Cancer Control 26(1) (2019) 1073274819863802. [5] M. Borner, M. Castiglione, J. Triller, H.U. Baer, M. Soucek, L. Blumgart, K. Brunner, Arena: Considerable side effects of chemoembolization for colorectal carcinoma metastatic to the liver, Annals of oncology 3(2) (1992) 113-115. [6] K. Memon, R.J. Lewandowski, L. Kulik, A. Riaz, M.F. Mulcahy, R. Salem, Radioembolization for primary and metastatic liver cancer, In Seminars in radiation oncology, WB Saunders. 21(4) (2011) 294-302. [7] I. Gory, M. Fink, S. Bell, P. Gow, A. Nicoll, V. Knight, W. Kemp, Radiofrequency ablation versus resection for the treatment of early stage hepatocellular carcinoma: A multicenter Australian study, Scandinavian journal of gastroenterology 50(5) (2015) 567-576. [8] H.M. Luu, C. Klink, W. Niessen, A. Moelker, T. Van Walsum, Non-rigid registration of liver CT images for CT-guided ablation of liver tumors. PloS one, 11(9) 92016) e0161600. [9] G. Gunay, M.H. Luu, A. Moelker, T. Van Walsum, S. Klein, Semiautomated registration of pre‐and intraoperative CT for image‐guided percutaneous liver tumor ablation interventions, Medical physics 44(7) (2017) 3718-3725. [10] A. Gotra, L. Sivakumaran, G. Chartrand, N. Vu, F. Vandenbroucke-Menu, C. Kauffmann, A. Tang, Liver segmentation: Indications, techniques and future directions, Insights into imaging 8(4) (2017) 377-392. https://doi.org/10.1007/s13244-017-0558-1. [11] T. Heimann, B. Van Ginneken, M.A. Styner, Y. Arzhaeva, V. Aurich, C. Bauer, F. Bello, Comparison and evaluation of methods for liver segmentation from CT datasets, IEEE transactions on medical imaging 28(8) (2009) 1251-1265. [12] O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, In International Conference on Medical image computing and computer-assisted intervention, Springer, Cham, 2015, pp. 234-241. [13] F. Milletari, N. Navab, S.A. Ahmadi, October, V-net: Fully convolutional neural networks for volumetric medical image segmentation, In 2016 Fourth International Conference on 3D Vision (3DV) IEEE, 2016, pp. 565-571. [14] P.F. Christ, F. Ettlinger, F. Grün, M.A. Elshaera, J. Lipkova, S. Schlecht, M. Rempfler, Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks, arXiv preprint arXiv:1702.05970, 2017. [15] P.F. Christ, M.E.A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, H. Sommer, Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. In International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 2016, pp. 415-423. [16] H. Meine, G. Chlebus, M. Ghafoorian, I. Endo, A. Schenk, Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT. arXiv preprint arXiv, 2018, pp. 1810.04017. [17] X. Li, H. Chen, X. Qi, Q. Dou, C.W. Fu, P.A. Heng, H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes, IEEE transactions on medical imaging, 37(12) (2018) 2663-2674. [18] M. Bellver, K.K. Maninis, J. Pont-Tuset, X. Giró-i-Nieto, J. Torres, L. Van Gool, Detection-aided liver lesion segmentation using deep learning, ArXiv preprint arXiv:1711.11069, 2017. [19] H.S. Hoang, C.P. Pham, D. Franklin, T. Van Walsum, M.H. Luu, An Evaluation of CNN-based Liver Segmentation Methods using Multi-types of CT Abdominal Images from Multiple Medical Centers, In 2019 19th International Symposium on Communications and Information Technologies (ISCIT), IEEE, September, 2019, pp. 20-25. [20] H. Samet, M. Tamminen, Efficient component labeling of images of arbitrary dimension represented by linear bintrees, IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(4) (1988) 579-586. [21] P. Bilic, P.F. Christ, E. Vorontsov, G. Chlebus, H. Chen, Q. Dou, S. Kadoury, The liver tumor segmentation benchmark (lits), ArXiv preprint arXiv, 2019, 1901.04056. [22] H.M. Luu, A. Moelker, S. Klein, W. Niessen, T. Van Walsum, Quantification of nonrigid liver deformation in radiofrequency ablation interventions using image registration, Physics in Medicine & Biology 63(17) (2018) 175005. [23] A.A. Novikov, D. Major, M. Wimmer, D. Lenis, K. Bühler, Deep Sequential Segmentation of Organs in Volumetric Medical Scans, IEEE transactions on medical imaging, 2018. [24] Y. Huo, J.G. Terry, J. Wang, S. Nair, A. Lasko, B.I. Freedman, B.A. Landman, Fully Automatic Liver Attenuation Estimation combing CNN Segmentation and Morphological Operations, Medical physics, 2019. [25] N. Gruber, S. Antholzer, W. Jaschke, C. Kremser, M. Haltmeier, A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation, ArXiv preprint arXiv, 2019, pp. 1902.07971. [26] S. Chen, K. Ma, Y. Zheng, Med3D: Transfer Learning for 3D Medical Image Analysis, ArXiv preprint arXiv, 2019, pp. 1904.00625. [27] W. Tang, D. Zou, S. Yang, J. Shi, DSL: Automatic Liver Segmentation with Faster R-CNN and DeepLab, In International Conference on Artificial Neural Networks, Springer, Cham, 2018, pp. 137-147.