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Refinado por: Nome da Publicação: Medical Physics remover
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Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets
Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets
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Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets

Cha, Kenny H. ; Hadjiiski, Lubomir ; Samala, Ravi K. ; Chan, Heang-Ping ; Caoili, Elaine M. ; Cohan, Richard H.

Medical physics (Lancaster), 2016-04, Vol.43 (4), p.1882-1896 [Periódico revisado por pares]

United States: American Association of Physicists in Medicine

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Vision 20/20: Perspectives on automated image segmentation for radiotherapy
Vision 20/20: Perspectives on automated image segmentation for radiotherapy
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Vision 20/20: Perspectives on automated image segmentation for radiotherapy

Sharp, Gregory ; Fritscher, Karl D. ; Pekar, Vladimir ; Peroni, Marta ; Shusharina, Nadya ; Veeraraghavan, Harini ; Yang, Jinzhong

Medical physics (Lancaster), 2014-05, Vol.41 (5), p.050902-n/a [Periódico revisado por pares]

United States: American Association of Physicists in Medicine

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3
Normalized metal artifact reduction (NMAR) in computed tomography
Normalized metal artifact reduction (NMAR) in computed tomography
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Normalized metal artifact reduction (NMAR) in computed tomography

Meyer, Esther ; Raupach, Rainer ; Lell, Michael ; Schmidt, Bernhard ; Kachelrieß, Marc

Medical physics (Lancaster), 2010-10, Vol.37 (10), p.5482-5493 [Periódico revisado por pares]

United States: American Association of Physicists in Medicine

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4
Interleaved 3D‐CNNs for joint segmentation of small‐volume structures in head and neck CT images
Interleaved 3D‐CNNs for joint segmentation of small‐volume structures in head and neck CT images
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Interleaved 3D‐CNNs for joint segmentation of small‐volume structures in head and neck CT images

Ren, Xuhua ; Xiang, Lei ; Nie, Dong ; Shao, Yeqin ; Zhang, Huan ; Shen, Dinggang ; Wang, Qian

Medical physics (Lancaster), 2018-05, Vol.45 (5), p.2063-2075 [Periódico revisado por pares]

United States

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5
COVID‐19 CT image segmentation based on improved Res2Net
COVID‐19 CT image segmentation based on improved Res2Net
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COVID‐19 CT image segmentation based on improved Res2Net

Liu, Shangwang ; Tang, Xiufang ; Cai, Tongbo ; Zhang, Yangyang ; Wang, Changgeng

Medical physics (Lancaster), 2022-12, Vol.49 (12), p.7583-7595 [Periódico revisado por pares]

United States: John Wiley and Sons Inc

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6
Vision 20/20: Magnetic resonance imaging-guided attenuation correction in PET/MRI: Challenges, solutions, and opportunities
Vision 20/20: Magnetic resonance imaging-guided attenuation correction in PET/MRI: Challenges, solutions, and opportunities
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Vision 20/20: Magnetic resonance imaging-guided attenuation correction in PET/MRI: Challenges, solutions, and opportunities

Mehranian, Abolfazl ; Arabi, Hossein ; Zaidi, Habib

Medical physics (Lancaster), 2016-03, Vol.43 (3), p.1130-1155 [Periódico revisado por pares]

United States: American Association of Physicists in Medicine

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7
Evaluation of tumor‐derived MRI‐texture features for discrimination of molecular subtypes and prediction of 12‐month survival status in glioblastoma
Evaluation of tumor‐derived MRI‐texture features for discrimination of molecular subtypes and prediction of 12‐month survival status in glioblastoma
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Evaluation of tumor‐derived MRI‐texture features for discrimination of molecular subtypes and prediction of 12‐month survival status in glioblastoma

Yang, Dalu ; Rao, Ganesh ; Martinez, Juan ; Veeraraghavan, Ashok ; Rao, Arvind

Medical physics (Lancaster), 2015-11, Vol.42 (11), p.6725-6735 [Periódico revisado por pares]

United States: American Association of Physicists in Medicine

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8
Large scale validation of the M5L lung CAD on heterogeneous CT datasets
Large scale validation of the M5L lung CAD on heterogeneous CT datasets
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Large scale validation of the M5L lung CAD on heterogeneous CT datasets

Lopez Torres, E. ; Fiorina, E. ; Pennazio, F. ; Peroni, C. ; Saletta, M. ; Camarlinghi, N. ; Fantacci, M. E. ; Cerello, P.

Medical physics (Lancaster), 2015-04, Vol.42 (4), p.1477-1489 [Periódico revisado por pares]

United States: American Association of Physicists in Medicine

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9
Computer‐aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist‐based assessments
Computer‐aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist‐based assessments
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Computer‐aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist‐based assessments

Chang, Yongjun ; Paul, Anjan Kumar ; Kim, Namkug ; Baek, Jung Hwan ; Choi, Young Jun ; Ha, Eun Ju ; Lee, Kang Dae ; Lee, Hyoung Shin ; Shin, DaeSeock ; Kim, Nakyoung

Medical physics (Lancaster), 2016-01, Vol.43 (1), p.554-567 [Periódico revisado por pares]

United States: American Association of Physicists in Medicine

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10
Fully automatic multi‐organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks
Fully automatic multi‐organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks
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Fully automatic multi‐organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks

Tong, Nuo ; Gou, Shuiping ; Yang, Shuyuan ; Ruan, Dan ; Sheng, Ke

Medical physics (Lancaster), 2018-10, Vol.45 (10), p.4558-4567 [Periódico revisado por pares]

United States: Wiley Blackwell (John Wiley & Sons)

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