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3. Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT Scans

Author

Ni, Haomiao, Xue, Yuan, Wong, Kelvin, Volpi, John, Wong, Stephen T. C., Wang, James Z., and Huang, Xiaolei

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Accurate infarct segmentation in non-contrast CT (NCCT) images is a crucial step toward computer-aided acute ischemic stroke (AIS) assessment. In clinical practice, bilateral symmetric comparison of brain hemispheres is usually used to locate pathological abnormalities. Recent research has explored asymmetries to assist with AIS segmentation. However, most previous symmetry-based work mixed different types of asymmetries when evaluating their contribution to AIS. In this paper, we propose a novel Asymmetry Disentanglement Network (ADN) to automatically separate pathological asymmetries and intrinsic anatomical asymmetries in NCCTs for more effective and interpretable AIS segmentation. ADN first performs asymmetry disentanglement based on input NCCTs, which produces different types of 3D asymmetry maps. Then a synthetic, intrinsic-asymmetry-compensated and pathology-asymmetry-salient NCCT volume is generated and later used as input to a segmentation network. The training of ADN incorporates domain knowledge and adopts a tissue-type aware regularization loss function to encourage clinically-meaningful pathological asymmetry extraction. Coupled with an unsupervised 3D transformation network, ADN achieves state-of-the-art AIS segmentation performance on a public NCCT dataset. In addition to the superior performance, we believe the learned clinically-interpretable asymmetry maps can also provide insights towards a better understanding of AIS assessment. Our code is available at https://github.com/nihaomiao/MICCAI22_ADN., Comment: MICCAI 2022

Published
2022

4. Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation

Author

Ou, Yanglan, Yuan, Ye, Huang, Xiaolei, Wong, Stephen T. C., Volpi, John, Wang, James Z., and Wong, Kelvin

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

We present a new encoder-decoder Vision Transformer architecture, Patcher, for medical image segmentation. Unlike standard Vision Transformers, it employs Patcher blocks that segment an image into large patches, each of which is further divided into small patches. Transformers are applied to the small patches within a large patch, which constrains the receptive field of each pixel. We intentionally make the large patches overlap to enhance intra-patch communication. The encoder employs a cascade of Patcher blocks with increasing receptive fields to extract features from local to global levels. This design allows Patcher to benefit from both the coarse-to-fine feature extraction common in CNNs and the superior spatial relationship modeling of Transformers. We also propose a new mixture-of-experts (MoE) based decoder, which treats the feature maps from the encoder as experts and selects a suitable set of expert features to predict the label for each pixel. The use of MoE enables better specializations of the expert features and reduces interference between them during inference. Extensive experiments demonstrate that Patcher outperforms state-of-the-art Transformer- and CNN-based approaches significantly on stroke lesion segmentation and polyp segmentation. Code for Patcher is released with publication to facilitate future research., Comment: MICCAI 2022

Published
2022

5. DeepStroke: An Efficient Stroke Screening Framework for Emergency Rooms with Multimodal Adversarial Deep Learning

Author

Cai, Tongan, Ni, Haomiao, Yu, Mingli, Huang, Xiaolei, Wong, Kelvin, Volpi, John, Wang, James Z., and Wong, Stephen T. C.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

In an emergency room (ER) setting, stroke triage or screening is a common challenge. A quick CT is usually done instead of MRI due to MRI's slow throughput and high cost. Clinical tests are commonly referred to during the process, but the misdiagnosis rate remains high. We propose a novel multimodal deep learning framework, DeepStroke, to achieve computer-aided stroke presence assessment by recognizing patterns of minor facial muscles incoordination and speech inability for patients with suspicion of stroke in an acute setting. Our proposed DeepStroke takes one-minute facial video data and audio data readily available during stroke triage for local facial paralysis detection and global speech disorder analysis. Transfer learning was adopted to reduce face-attribute biases and improve generalizability. We leverage a multi-modal lateral fusion to combine the low- and high-level features and provide mutual regularization for joint training. Novel adversarial training is introduced to obtain identity-free and stroke-discriminative features. Experiments on our video-audio dataset with actual ER patients show that DeepStroke outperforms state-of-the-art models and achieves better performance than both a triage team and ER doctors, attaining a 10.94% higher sensitivity and maintaining 7.37% higher accuracy than traditional stroke triage when specificity is aligned. Meanwhile, each assessment can be completed in less than six minutes, demonstrating the framework's great potential for clinical translation.

Published
2021

6. Multimodal Breast Lesion Classification Using Cross-Attention Deep Networks

Author

Vo, Hung Q., Yuan, Pengyu, He, Tiancheng, Wong, Stephen T. C., and Nguyen, Hien V.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Accurate breast lesion risk estimation can significantly reduce unnecessary biopsies and help doctors decide optimal treatment plans. Most existing computer-aided systems rely solely on mammogram features to classify breast lesions. While this approach is convenient, it does not fully exploit useful information in clinical reports to achieve the optimal performance. Would clinical features significantly improve breast lesion classification compared to using mammograms alone? How to handle missing clinical information caused by variation in medical practice? What is the best way to combine mammograms and clinical features? There is a compelling need for a systematic study to address these fundamental questions. This paper investigates several multimodal deep networks based on feature concatenation, cross-attention, and co-attention to combine mammograms and categorical clinical variables. We show that the proposed architectures significantly increase the lesion classification performance (average area under ROC curves from 0.89 to 0.94). We also evaluate the model when clinical variables are missing.

Published
2021
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7. LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images

Author

Ou, Yanglan, Yuan, Ye, Huang, Xiaolei, Wong, Kelvin, Volpi, John, Wang, James Z., and Wong, Stephen T. C.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Diffusion-weighted (DW) magnetic resonance imaging is essential for the diagnosis and treatment of ischemic stroke. DW images (DWIs) are usually acquired in multi-slice settings where lesion areas in two consecutive 2D slices are highly discontinuous due to large slice thickness and sometimes even slice gaps. Therefore, although DWIs contain rich 3D information, they cannot be treated as regular 3D or 2D images. Instead, DWIs are somewhere in-between (or 2.5D) due to the volumetric nature but inter-slice discontinuities. Thus, it is not ideal to apply most existing segmentation methods as they are designed for either 2D or 3D images. To tackle this problem, we propose a new neural network architecture tailored for segmenting highly-discontinuous 2.5D data such as DWIs. Our network, termed LambdaUNet, extends UNet by replacing convolutional layers with our proposed Lambda+ layers. In particular, Lambda+ layers transform both intra-slice and inter-slice context around a pixel into linear functions, called lambdas, which are then applied to the pixel to produce informative 2.5D features. LambdaUNet is simple yet effective in combining sparse inter-slice information from adjacent slices while also capturing dense contextual features within a single slice. Experiments on a unique clinical dataset demonstrate that LambdaUNet outperforms existing 3D/2D image segmentation methods including recent variants of UNet. Code for LambdaUNet is released with the publication to facilitate future research.

Published
2021

10. Interferon-gamma is quintessential for NOS2 and COX2 expression in ER- breast tumors that lead to poor outcome

Author

Cheng, Robert Y. S., Ridnour, Lisa A., Wink, Adelaide L., Gonzalez, Ana L., Femino, Elise L., Rittscher, Helene, Somasundaram, Veena, Heinz, William F., Coutinho, Leandro, Rangel, M. Cristina, Edmondson, Elijah F., Butcher, Donna, Kinders, Robert J., Li, Xiaoxian, Wong, Stephen T. C., McVicar, Daniel W., Anderson, Stephen K., Pore, Milind, Hewitt, Stephen M., Billiar, Timothy R., Glynn, Sharon A., Chang, Jenny C., Lockett, Stephen J., Ambs, Stefan, and Wink, David A.

Published
2023
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18. Toward Rapid Stroke Diagnosis with Multimodal Deep Learning

Author

Yu, Mingli, Cai, Tongan, Huang, Xiaolei, Wong, Kelvin, Volpi, John, Wang, James Z., Wong, Stephen T. C., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Martel, Anne L., editor, Abolmaesumi, Purang, editor, Stoyanov, Danail, editor, Mateus, Diana, editor, Zuluaga, Maria A., editor, Zhou, S. Kevin, editor, Racoceanu, Daniel, editor, and Joskowicz, Leo, editor

Published
2020
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19. Automated catheter segmentation and tip detection in cerebral angiography with topology-aware geometric deep learning.

Author

Ghosh, Rahul, Wong, Kelvin, Zhang, Yi Jonathan, Britz, Gavin W., and Wong, Stephen T. C.

Subjects
CEREBRAL angiography, MATHEMATICS, HUMAN services programs, RESEARCH funding, ARTIFICIAL intelligence, CATHETERIZATION, DESCRIPTIVE statistics, LONGITUDINAL method, DEEP learning, ARTIFICIAL neural networks, CATHETERS, DIGITAL image processing, COMPARATIVE studies, FLUOROSCOPY
Abstract

Background Visual perception of catheters and guidewires on x-ray fluoroscopy is essential for neurointervention. Endovascular robots with teleoperation capabilities are being developed, but they cannot 'see' intravascular devices, which precludes artificial intelligence (AI) augmentation that could improve precision and autonomy. Deep learning has not been explored for neurointervention and prior works in cardiovascular scenarios are inadequate as they only segment device tips, while neurointervention requires segmentation of the entire structure due to coaxial devices. Therefore, this study develops an automatic and accurate image-based catheter segmentation method in cerebral angiography using deep learning. Methods Catheters and guidewires were manually annotated on 3831 fluoroscopy frames collected prospectively from 40 patients undergoing cerebral angiography. We proposed a topology-aware geometric deep learning method (TAG-DL) and compared it with the state-of-the-art deep learning segmentation models, UNet, nnUNet and TransUNet. All models were trained on frontal view sequences and tested on both frontal and lateral view sequences from unseen patients. Results were assessed with centerline Dice score and tip-distance error. Results The TAG-DL and nnUNet models outperformed TransUNet and UNet. The best performing model was nnUNet, achieving a mean centerline-Dice score of 0.98 ±0.01 and a median tip-distance error of 0.43 (IQR 0.88) mm. Incorporating digital subtraction masks, with or without contrast, significantly improved performance on unseen patients, further enabling exceptional performance on lateral view fluoroscopy despite not being trained on this view. Conclusions These results are the first step towards AI augmentation for robotic neurointervention that could amplify the reach, productivity, and safety of a limited neurointerventional workforce. [ABSTRACT FROM AUTHOR]

Published
2024
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21. TP-0184 inhibits FLT3/ACVR1 to overcome FLT3 inhibitor resistance and hinder AML growth synergistically with venetoclax

Author

Tyagi, Anudishi, primary, Jaggupilli, Appalaraju, additional, Ly, Stanley, additional, Yuan, Bin, additional, El-Dana, Fouad, additional, Hegde, Venkatesh L., additional, Anand, Vivek, additional, Kumar, Bijender, additional, Puppala, Mamta, additional, Yin, Zheng, additional, Wong, Stephen T. C., additional, Mollard, Alexis, additional, Vankayalapati, Hariprasad, additional, Foulks, Jason M., additional, Warner, Steven L., additional, Daver, Naval, additional, Borthakur, Gautam, additional, and Battula, V. Lokesh, additional

Published
2023
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24. A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms

Author

Ezeana, Chika F., primary, He, Tiancheng, additional, Patel, Tejal A., additional, Kaklamani, Virginia, additional, Elmi, Maryam, additional, Brigmon, Erika, additional, Otto, Pamela M., additional, Kist, Kenneth A., additional, Speck, Heather, additional, Wang, Lin, additional, Ensor, Joe, additional, Shih, Ya-Chen T., additional, Kim, Bumyang, additional, Pan, I-Wen, additional, Cohen, Adam L., additional, Kelley, Kristen, additional, Spak, David, additional, Yang, Wei T., additional, Chang, Jenny C., additional, and Wong, Stephen T. C., additional

Published
2023
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27. Data from Mammary Precancerous Stem and Non-Stem Cells Evolve into Cancers of Distinct Subtypes

Author

Bu, Wen, primary, Liu, Zhenyu, primary, Jiang, Weiyu, primary, Nagi, Chandandeep, primary, Huang, Shixia, primary, Edwards, Dean P., primary, Jo, Eunji, primary, Mo, Qianxing, primary, Creighton, Chad J., primary, Hilsenbeck, Susan G., primary, Leavitt, Andrew D., primary, Lewis, Michael T., primary, Wong, Stephen T. C., primary, and Li, Yi, primary

Published
2023
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28. Figure S8 from Mammary Precancerous Stem and Non-Stem Cells Evolve into Cancers of Distinct Subtypes

Author

Bu, Wen, primary, Liu, Zhenyu, primary, Jiang, Weiyu, primary, Nagi, Chandandeep, primary, Huang, Shixia, primary, Edwards, Dean P., primary, Jo, Eunji, primary, Mo, Qianxing, primary, Creighton, Chad J., primary, Hilsenbeck, Susan G., primary, Leavitt, Andrew D., primary, Lewis, Michael T., primary, Wong, Stephen T. C., primary, and Li, Yi, primary

Published
2023
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29. Transductive Maximum Margin Classification of ADHD Using Resting State fMRI

Author

Wang, Lei, Li, Danping, He, Tiancheng, Wong, Stephen T. C., Xue, Zhong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wang, Li, editor, Adeli, Ehsan, editor, Wang, Qian, editor, Shi, Yinghuan, editor, and Suk, Heung-Il, editor

Published
2016
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30. CD4+T cell immunity is dependent on an intrinsic stem-like program

Author

Zou, Dawei, Yin, Zheng, Yi, Stephanie G., Wang, Guohua, Guo, Yang, Xiao, Xiang, Li, Shuang, Zhang, Xiaolong, Gonzalez, Nancy M., Minze, Laurie J., Wang, Lin, Wong, Stephen T. C., Osama Gaber, A., Ghobrial, Rafik M., Li, Xian C., and Chen, Wenhao

Abstract

CD4+T cells are central to various immune responses, but the molecular programs that drive and maintain CD4+T cell immunity are not entirely clear. Here we identify a stem-like program that governs the CD4+T cell response in transplantation models. Single-cell-transcriptomic analysis revealed that naive alloantigen-specific CD4+T cells develop into TCF1hieffector precursor (TEP) cells and TCF1−CXCR6+effectors in transplant recipients. The TCF1−CXCR6+CD4+effectors lose proliferation capacity and do not reject allografts upon adoptive transfer into secondary hosts. By contrast, the TCF1hiCD4+TEPcells have dual features of self-renewal and effector differentiation potential, and allograft rejection depends on continuous replenishment of TCF1−CXCR6+effectors from TCF1hiCD4+TEPcells. Mechanistically, TCF1 sustains the CD4+TEPcell population, whereas the transcription factor IRF4 and the glycolytic enzyme LDHA govern the effector differentiation potential of CD4+TEPcells. Deletion of IRF4 or LDHA in T cells induces transplant acceptance. These findings unravel a stem-like program that controls the self-renewal capacity and effector differentiation potential of CD4+TEPcells and have implications for T cell-related immunotherapies.

Published
2024
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31. TP-0184 inhibits FLT3/ACVR1to overcome FLT3 inhibitor resistance and hinder AML growth synergistically with venetoclax

Author

Tyagi, Anudishi, Jaggupilli, Appalaraju, Ly, Stanley, Yuan, Bin, El-Dana, Fouad, Hegde, Venkatesh L., Anand, Vivek, Kumar, Bijender, Puppala, Mamta, Yin, Zheng, Wong, Stephen T. C., Mollard, Alexis, Vankayalapati, Hariprasad, Foulks, Jason M., Warner, Steven L., Daver, Naval, Borthakur, Gautam, and Battula, V. Lokesh

Abstract

We identified activin A receptor type I (ACVR1), a member of the TGF-β superfamily, as a factor favoring acute myeloid leukemia (AML) growth and a new potential therapeutic target. ACVR1is overexpressed in FLT3-mutated AML and inhibition of ACVR1expression sensitized AML cells to FLT3 inhibitors. We developed a novel ACVR1inhibitor, TP-0184, which selectively caused growth arrest in FLT3-mutated AML cell lines. Molecular docking and in vitro kinase assays revealed that TP-0184 binds to both ACVR1and FLT3 with high affinity and inhibits FLT3/ACVR1downstream signaling. Treatment with TP-0184 or in combination with BCL2 inhibitor, venetoclax dramatically inhibited leukemia growth in FLT3-mutated AML cell lines and patient-derived xenograft models in a dose-dependent manner. These findings suggest that ACVR1is a novel biomarker and plays a role in AML resistance to FLT3 inhibitors and that FLT3/ACVR1dual inhibitor TP-0184 is a novel potential therapeutic tool for AML with FLT3mutations.

Published
2024
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35. Toward a ‘Brain New Deal’ to Harness the Potential of Artificial Intelligence

Author

Eyre, Harris A., Dawson, Walter D., Abbott, Ryan, Silverman, Karen, Chen, Shuo, Ayadi, Rym, Carnevale, Steve, Wong, Stephen T. C., Berk, Michael, Nanda, Upali, Ling, Geoffrey F. L., Tempest, Michelle, Occhipinti, Jo-An, Shah, Seema, Mahfouz, Julia, Fu, Ernestine, MacRae, Ian, Esquivel, Gabriel, Edmonds, Theo, Jacka, Felice, Hynes, William, Wickerson, Grace, and Swieboda, Pawel

Published
2023
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37. Simultaneous Tensor and Fiber Registration (STFR) for Diffusion Tensor Images of the Brain

Author

Xue, Zhong, Wong, Stephen T. C., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Liao, Hongen, editor, Linte, Cristian A., editor, Masamune, Ken, editor, Peters, Terry M., editor, and Zheng, Guoyan, editor

Published
2013
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38. A Novel Geodesic Distance Based Clustering Approach to Delineating Boundaries of Touching Cells

Author

Chen, Xu, Zhu, Yanqiao, Li, Fuhai, Zheng, Zeyi, Chang, Eric, Ma, Jinwen, Wong, Stephen T. C., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Guo, Chengan, editor, Hou, Zeng-Guang, editor, and Zeng, Zhigang, editor

Published
2013
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39. Extracting Key Pathways from Gene Signature and Genetic Aberrations in Subtypes of Cancer

Author

Chen, Peikai, Fan, Yubo, Man, Tsz-kwong, Lau, Ching C., Hung, Y. S., Wong, Stephen T. -C., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael S., editor, Peterson, Leif E., editor, Masulli, Francesco, editor, and Russo, Giuseppe, editor

Published
2013
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40. Automated Nuclear Segmentation of Coherent Anti-Stokes Raman Scattering Microscopy Images by Coupling Superpixel Context Information with Artificial Neural Networks

Author

Hammoudi, Ahmad A., Li, Fuhai, Gao, Liang, Wang, Zhiyong, Thrall, Michael J., Massoud, Yehia, Wong, Stephen T. C., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Suzuki, Kenji, editor, Wang, Fei, editor, Shen, Dinggang, editor, and Yan, Pingkun, editor

Published
2011
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43. Optimal Live Cell Tracking for Cell Cycle Study Using Time-Lapse Fluorescent Microscopy Images

Author

Li, Fuhai, Zhou, Xiaobo, Wong, Stephen T. C., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Fei, editor, Yan, Pingkun, editor, Suzuki, Kenji, editor, and Shen, Dinggang, editor

Published
2010
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44. Motion Artifact Correction of Multi-Photon Imaging of Awake Mice Models Using Speed Embedded HMM

Author

Chen, Taoyi, Xue, Zhong, Wang, Changhong, Qu, Zhenshen, Wong, Kelvin K., Wong, Stephen T. C., Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Jiang, Tianzi, editor, Navab, Nassir, editor, Pluim, Josien P. W., editor, and Viergever, Max A., editor

Published
2010
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45. Online 4-D CT Estimation for Patient-Specific Respiratory Motion Based on Real-Time Breathing Signals

Author

He, Tiancheng, Xue, Zhong, Xie, Weixin, Wong, Stephen T. C., Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Jiang, Tianzi, editor, Navab, Nassir, editor, Pluim, Josien P. W., editor, and Viergever, Max A., editor

Published
2010
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46. A Motion Correction Algorithm for Microendoscope Video Computing in Image-Guided Intervention

Author

He, Tiancheng, Xue, Zhong, Xie, Weixin, Wong, Solomon, Wong, Kelvin, Alvarado, Miguel Valdivia y, Wong, Stephen T. C., Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Liao, Hongen, editor, Edwards, P. J. 'Eddie", editor, Pan, Xiaochuan, editor, Fan, Yong, editor, and Yang, Guang-Zhong, editor

Published
2010
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47. A Minimally Invasive Multimodality Image-Guided (MIMIG) Molecular Imaging System for Peripheral Lung Cancer Intervention and Diagnosis

Author

He, Tiancheng, Xue, Zhong, Wong, Kelvin K., Valdivia y Alvarado, Miguel, Zhang, Yong, Xie, Weixin, Wong, Stephen T. C., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Navab, Nassir, editor, and Jannin, Pierre, editor

Published
2010
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48. Cell Segmentation Using Front Vector Flow Guided Active Contours

Author

Li, Fuhai, Zhou, Xiaobo, Zhao, Hong, Wong, Stephen T. C., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yang, Guang-Zhong, editor, Hawkes, David, editor, Rueckert, Daniel, editor, Noble, Alison, editor, and Taylor, Chris, editor

Published
2009
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49. Simultaneous Consideration of Spatial Deformation and Tensor Orientation in Diffusion Tensor Image Registration Using Local Fast Marching Patterns

Author

Li, Hai, Xue, Zhong, Guo, Lei, Wong, Stephen T. C., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Prince, Jerry L., editor, Pham, Dzung L., editor, and Myers, Kyle J., editor

Published
2009
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