The following publications are possibly variants of this publication:
- Air-Tissue Boundary Segmentation in Real-Time Magnetic Resonance Imaging Video Using Semantic Segmentation with Fully Convolutional NetworksC. A. Valliappan, Renuka Mannem, Prasanta Kumar Ghosh. interspeech 2018: 3132-3136 [doi]
- Air-tissue Boundary Segmentation in Real Time Magnetic Resonance Imaging Video Using a Convolutional Encoder-decoder NetworkRenuka Mannem, Prasanta Kumar Ghosh. icassp 2019: 5941-5945 [doi]
- An Improved Air Tissue Boundary Segmentation Technique for Real Time Magnetic Resonance Imaging Video Using SegnetC. A. Valliappan, Avinash Kumar, Renuka Mannem, Girija Ramesan Karthik, Prasanta Kumar Ghosh. icassp 2019: 5921-5925 [doi]
- Air tissue boundary segmentation using regional loss in real-time Magnetic Resonance Imaging video for speech productionAnwesha Roy, Varun Belagali, Prasanta Kumar Ghosh. interspeech 2022: 3113-3117 [doi]
- A SegNet Based Image Enhancement Technique for Air-Tissue Boundary Segmentation in Real-Time Magnetic Resonance Imaging VideoRenuka Mannem, Valliappan Ca, Prasanta Kumar Ghosh. ncc 2019: 1-6 [doi]
- A Supervised Air-Tissue Boundary Segmentation Technique in Real-Time Magnetic Resonance Imaging Video Using a Novel Measure of Contrast and Dynamic ProgrammingAdvait Koparkar, Prasanta Kumar Ghosh. icassp 2018: 5004-5008 [doi]
- A deep neural network based correction scheme for improved air-tissue boundary prediction in real-time magnetic resonance imaging videoRenuka Mannem, Prasanta Kumar Ghosh. csl, 66:101160, 2021. [doi]