1 code implementation • 15 Mar 2024 • Jin Yang, Peijie Qiu, Yichi Zhang, Daniel S. Marcus, Aristeidis Sotiras
D-Net is able to effectively utilize a multi-scale large receptive field and adaptively harness global contextual information.
no code implementations • 12 Mar 2024 • Jin Yang, Daniel S. Marcus, Aristeidis Sotiras
We evaluated Dynamic U-Net in two abdominal multi-organ segmentation benchmarks.
1 code implementation • 25 Jan 2023 • Zhijian Yang, Junhao Wen, Ahmed Abdulkadir, Yuhan Cui, Guray Erus, Elizabeth Mamourian, Randa Melhem, Dhivya Srinivasan, Sindhuja T. Govindarajan, Jiong Chen, Mohamad Habes, Colin L. Masters, Paul Maruff, Jurgen Fripp, Luigi Ferrucci, Marilyn S. Albert, Sterling C. Johnson, John C. Morris, Pamela Lamontagne, Daniel S. Marcus, Tammie L. S. Benzinger, David A. Wolk, Li Shen, Jingxuan Bao, Susan M. Resnick, Haochang Shou, Ilya M. Nasrallah, Christos Davatzikos
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases.
no code implementations • 7 Oct 2022 • Satrajit Chakrabarty, Pamela Lamontagne, Joshua Shimony, Daniel S. Marcus, Aristeidis Sotiras
A 2. 5D hybrid convolutional neural network was proposed to simultaneously localize the tumor and classify its molecular status by leveraging imaging features from MR scans and prior knowledge features from clinical records and tumor location.
2 code implementations • 6 Oct 2022 • Satrajit Chakrabarty, Syed Amaan Abidi, Mina Mousa, Mahati Mokkarala, Isabelle Hren, Divya Yadav, Matthew Kelsey, Pamela Lamontagne, John Wood, Michael Adams, Yuzhuo Su, Sherry Thorpe, Caroline Chung, Aristeidis Sotiras, Daniel S. Marcus
Mean Dice scores were 0. 882 ($\pm$0. 244) and 0. 977 ($\pm$0. 04) for whole tumor segmentation for WUSM and MDA, respectively.
no code implementations • 13 Dec 2021 • Bhakti Baheti, Satrajit Chakrabarty, Hamed Akbari, Michel Bilello, Benedikt Wiestler, Julian Schwarting, Evan Calabrese, Jeffrey Rudie, Syed Abidi, Mina Mousa, Javier Villanueva-Meyer, Brandon K. K. Fields, Florian Kofler, Russell Takeshi Shinohara, Juan Eugenio Iglesias, Tony C. W. Mok, Albert C. S. Chung, Marek Wodzinski, Artur Jurgas, Niccolo Marini, Manfredo Atzori, Henning Muller, Christoph Grobroehmer, Hanna Siebert, Lasse Hansen, Mattias P. Heinrich, Luca Canalini, Jan Klein, Annika Gerken, Stefan Heldmann, Alessa Hering, Horst K. Hahn, Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim, Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert, Javid Abderezaei, Aymeric Pionteck, Agamdeep Chopra, Mehmet Kurt, Kewei Yan, Yonghong Yan, Zhe Tang, Jianqiang Ma, Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Mohit Meena, Saqib Shamsi, Amit Sethi, Nicholas J. Tustison, Brian B. Avants, Philip Cook, James C. Gee, Lin Tian, Hastings Greer, Marc Niethammer, Andrew Hoopes, Malte Hoffmann, Adrian V. Dalca, Stergios Christodoulidis, Theo Estiene, Maria Vakalopoulou, Nikos Paragios, Daniel S. Marcus, Christos Davatzikos, Aristeidis Sotiras, Bjoern Menze, Spyridon Bakas, Diana Waldmannstetter
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
no code implementations • 1 Nov 2021 • Rajarajeswari Muthusivarajan, Adrian Celaya, Joshua P. Yung, Satish Viswanath, Daniel S. Marcus, Caroline Chung, David Fuentes
Deep neural networks with multilevel connections process input data in complex ways to learn the information. A networks learning efficiency depends not only on the complex neural network architecture but also on the input training images. Medical image segmentation with deep neural networks for skull stripping or tumor segmentation from magnetic resonance images enables learning both global and local features of the images. Though medical images are collected in a controlled environment, there may be artifacts or equipment based variance that cause inherent bias in the input set. In this study, we investigated the correlation between the image quality metrics of MR images with the neural network segmentation accuracy. For that we have used the 3D DenseNet architecture and let the network trained on the same input but applying different methodologies to select the training data set based on the IQM values. The difference in the segmentation accuracy between models based on the random training inputs with IQM based training inputs shed light on the role of image quality metrics on segmentation accuracy. By running the image quality metrics to choose the training inputs, further we may tune the learning efficiency of the network and the segmentation accuracy.