Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Nasim Sirjani"'
Autor:
Vahid Ashkani Chenarlogh, Ali Shabanzadeh, Mostafa Ghelich Oghli, Nasim Sirjani, Sahar Farzin Moghadam, Ardavan Akhavan, Hossein Arabi, Isaac Shiri, Zahra Shabanzadeh, Morteza Sanei Taheri, Mohammad Kazem Tarzamni
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-17 (2022)
Abstract We introduced Double Attention Res-U-Net architecture to address medical image segmentation problem in different medical imaging system. Accurate medical image segmentation suffers from some challenges including, difficulty of different inte
Externí odkaz:
https://doaj.org/article/ec06e4b4716f49a38a7483bfd1353dc7
Autor:
Nasim Sirjani, Shakiba Moradi, Mostafa Ghelich Oghli, Ali Hosseinsabet, Azin Alizadehasl, Mona Yadollahi, Isaac Shiri, Ali Shabanzadeh
Publikováno v:
Insights into Imaging, Vol 13, Iss 1, Pp 1-14 (2022)
Abstract Background Accurate cardiac volume and function assessment have valuable and significant diagnostic implications for patients suffering from ventricular dysfunction and cardiovascular disease. This study has focused on finding a reliable ass
Externí odkaz:
https://doaj.org/article/f8df71dfa2ef4d3abd577d8dc269f78c
Autor:
Ali Shabanzadeh, Mostafa Ghelich Oghli, Hossein Arabi, Habib Zaidi, Morteza Sanei Taheri, Shakiba Moradi, Nasim Sirjani, Reza Gerami, Isaac Shiri, Payam Ghaderi
Publikováno v:
Ghelich Oghli, M, Shabanzadeh, A, Moradi, S, Sirjani, N, Gerami, R, Ghaderi, P, Sanei Taheri, M, Shiri, I, Arabi, H & Zaidi, H 2021, ' Automatic fetal biometry prediction using a novel deep convolutional network architecture ', Physica Medica, vol. 88, pp. 127-137 . https://doi.org/10.1016/j.ejmp.2021.06.020
Physica Medica, Vol. 88 (2021) pp. 127-137
Physica medica-European journal of medical physics, 88, 127-137. ELSEVIER SCI LTD
Physica Medica, Vol. 88 (2021) pp. 127-137
Physica medica-European journal of medical physics, 88, 127-137. ELSEVIER SCI LTD
Purpose Fetal biometric measurements face a number of challenges, including the presence of speckle, limited soft-tissue contrast and difficulties in the presence of low amniotic fluid. This work proposes a convolutional neural network for automatic
Autor:
Nasim Sirjani, Mostafa Ghelich Oghli, Mohammad Kazem Tarzamni, Masoumeh Gity, Ali Shabanzadeh, Payam Ghaderi, Isaac Shiri, Ardavan Akhavan, Mehri Faraji, Mostafa Taghipour
Publikováno v:
Physica Medica. 107:102560
Autor:
Vahid Ashkani Chenarlogh, Mostafa Ghelich Oghli, Ali Shabanzadeh, Nasim Sirjani, Ardavan Akhavan, Isaac Shiri, Hossein Arabi, Morteza Sanei Taheri, Mohammad Kazem Tarzamni
Publikováno v:
Ultrasonic imaging. 44(1)
U-Net based algorithms, due to their complex computations, include limitations when they are used in clinical devices. In this paper, we addressed this problem through a novel U-Net based architecture that called fast and accurate U-Net for medical i
Autor:
Nasim Sirjani, Shakiba Moradi, Mostafa Ghelich Oghli, Ali Hosseinsabet, Azin Alizadehasl, Mona Yadollahi, Isaac Shiri, Ali Shabanzadeh
Publikováno v:
Insights into imaging. 13(1)
Background Accurate cardiac volume and function assessment have valuable and significant diagnostic implications for patients suffering from ventricular dysfunction and cardiovascular disease. This study has focused on finding a reliable assistant to
Autor:
Vahid Ashkani Chenarlogh, Ali Shabanzadeh, Mostafa Ghelich Oghli, Nasim Sirjani, Sahar Farzin Moghadam, Ardavan Akhavan, Hossein Arabi, Isaac Shiri, Zahra Shabanzadeh, Morteza Sanei Taheri, Mohammad Kazem Tarzamni
Publikováno v:
Scientific reports. 12(1)
We introduced Double Attention Res-U-Net architecture to address medical image segmentation problem in different medical imaging system. Accurate medical image segmentation suffers from some challenges including, difficulty of different interest obje
Autor:
Mostafa Ghelich Oghli, Payam Ghaderi, Nasim Sirjani, Hossein Arabi, Shakiba Moradi, Reza Gerami, Isaac Shiri, Ali Shabanzadeh, Habib Zaidi
Publikováno v:
2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).
Gestational age (GA) is an illustrative indicator of fetal growth. The GA is estimated through biometric parameters, including the head circumference (HC) and Biparietal diameter (BPD). This paper proposes a deep learning-based method for automatic m