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1. Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers

Author

Singh, Vivek, Chaganti, Shikha, Siebert, Matthias, Rajesh, Soumya, Puiu, Andrei, Gopalan, Raj, Gramz, Jamie, Comaniciu, Dorin, and Kamen, Ali

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the screening method and frequency, primarily to optimize resource allocation by targeting screening towards individuals who draw most benefit. For most screening programs, age and clinical risk factors such as family history are part of the initial risk stratification algorithm. In this paper, we focus on developing a blood marker-based risk stratification approach, which could be used to identify patients with elevated cancer risk to be encouraged for taking a diagnostic test or participate in a screening program. We demonstrate that the combination of simple, widely available blood tests, such as complete blood count and complete metabolic panel, could potentially be used to identify patients at risk for colorectal, liver, and lung cancers with areas under the ROC curve of 0.76, 0.85, 0.78, respectively. Furthermore, we hypothesize that such an approach could not only be used as pre-screening risk assessment for individuals but also as population health management tool, for example to better interrogate the cancer risk in certain sub-populations.

Published
2024

2. Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Bi-parametric MRI Datasets

Author

Li, Hao, Liu, Han, von Busch, Heinrich, Grimm, Robert, Huisman, Henkjan, Tong, Angela, Winkel, David, Penzkofer, Tobias, Shabunin, Ivan, Choi, Moon Hyung, Yang, Qingsong, Szolar, Dieter, Shea, Steven, Coakley, Fergus, Harisinghani, Mukesh, Oguz, Ipek, Comaniciu, Dorin, Kamen, Ali, and Lou, Bin

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

Our hypothesis is that UDA using diffusion-weighted images, generated with a unified model, offers a promising and reliable strategy for enhancing the performance of supervised learning models in multi-site prostate lesion detection, especially when various b-values are present. This retrospective study included data from 5,150 patients (14,191 samples) collected across nine different imaging centers. A novel UDA method using a unified generative model was developed for multi-site PCa detection. This method translates diffusion-weighted imaging (DWI) acquisitions, including apparent diffusion coefficient (ADC) and individual DW images acquired using various b-values, to align with the style of images acquired using b-values recommended by Prostate Imaging Reporting and Data System (PI-RADS) guidelines. The generated ADC and DW images replace the original images for PCa detection. An independent set of 1,692 test cases (2,393 samples) was used for evaluation. The area under the receiver operating characteristic curve (AUC) was used as the primary metric, and statistical analysis was performed via bootstrapping. For all test cases, the AUC values for baseline SL and UDA methods were 0.73 and 0.79 (p<.001), respectively, for PI-RADS>=3, and 0.77 and 0.80 (p<.001) for PI-RADS>=4 PCa lesions. In the 361 test cases under the most unfavorable image acquisition setting, the AUC values for baseline SL and UDA were 0.49 and 0.76 (p<.001) for PI-RADS>=3, and 0.50 and 0.77 (p<.001) for PI-RADS>=4 PCa lesions. The results indicate the proposed UDA with generated images improved the performance of SL methods in multi-site PCa lesion detection across datasets with various b values, especially for images acquired with significant deviations from the PI-RADS recommended DWI protocol (e.g. with an extremely high b-value)., Comment: Accept at Radiology: Artificial Intelligence. Journal reference and external DOI will be added once published

Published
2024
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3. Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy

Author

Gao, Riqiang, Ghesu, Florin C., Arberet, Simon, Basiri, Shahab, Kuusela, Esa, Kraus, Martin, Comaniciu, Dorin, and Kamen, Ali

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Multiagent Systems
Abstract

In contemporary radiotherapy planning (RTP), a key module leaf sequencing is predominantly addressed by optimization-based approaches. In this paper, we propose a novel deep reinforcement learning (DRL) model termed as Reinforced Leaf Sequencer (RLS) in a multi-agent framework for leaf sequencing. The RLS model offers improvements to time-consuming iterative optimization steps via large-scale training and can control movement patterns through the design of reward mechanisms. We have conducted experiments on four datasets with four metrics and compared our model with a leading optimization sequencer. Our findings reveal that the proposed RLS model can achieve reduced fluence reconstruction errors, and potential faster convergence when integrated in an optimization planner. Additionally, RLS has shown promising results in a full artificial intelligence RTP pipeline. We hope this pioneer multi-agent RL leaf sequencer can foster future research on machine learning for RTP., Comment: Accepted by ICML 2024

Published
2024

5. A Wide-area, Low-latency, and Power-efficient 6-DoF Pose Tracking System for Rigid Objects

Author

Kim, Young-Ho, Kapoor, Ankur, Mansi, Tommaso, and Kamen, Ali

Subjects
Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

Position sensitive detectors (PSDs) offer possibility to track single active marker's two (or three) degrees of freedom (DoF) position with a high accuracy, while having a fast response time with high update frequency and low latency, all using a very simple signal processing circuit. However they are not particularly suitable for 6-DoF object pose tracking system due to lack of orientation measurement, limited tracking range, and sensitivity to environmental variation. We propose a novel 6-DoF pose tracking system for a rigid object tracking requiring a single active marker. The proposed system uses a stereo-based PSD pair and multiple Inertial Measurement Units (IMUs). This is done based on a practical approach to identify and control the power of Infrared-Light Emitting Diode (IR-LED) active markers, with an aim to increase the tracking work space and reduce the power consumption. Our proposed tracking system is validated with three different work space sizes and for static and dynamic positional accuracy using robotic arm manipulator with three different dynamic motion patterns. The results show that the static position root-mean-square (RMS) error is 0.6mm. The dynamic position RMS error is 0.7-0.9mm. The orientation RMS error is between 0.04 and 0.9 degree at varied dynamic motion. Overall, our proposed tracking system is capable of tracking a rigid object pose with sub-millimeter accuracy at the mid range of the work space and sub-degree accuracy for all work space under a lab setting.

Published
2021

6. Automated deep-learning system in the assessment of MRI-visible prostate cancer: comparison of advanced zoomed diffusion-weighted imaging and conventional technique

Author

Hu, Lei, Fu, Caixia, Song, Xinyang, Grimm, Robert, von Busch, Heinrich, Benkert, Thomas, Kamen, Ali, Lou, Bin, Huisman, Henkjan, Tong, Angela, Penzkofer, Tobias, Choi, Moon Hyung, Shabunin, Ivan, Winkel, David, Xing, Pengyi, Szolar, Dieter, Coakley, Fergus, Shea, Steven, Szurowska, Edyta, Guo, Jing-yi, Li, Liang, Li, Yue-hua, and Zhao, Jun-gong

Published
2023
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8. A concurrent, deep learning–based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists

Author

Labus, Sandra, Altmann, Martin M., Huisman, Henkjan, Tong, Angela, Penzkofer, Tobias, Choi, Moon Hyung, Shabunin, Ivan, Winkel, David J., Xing, Pengyi, Szolar, Dieter H., Shea, Steven M., Grimm, Robert, von Busch, Heinrich, Kamen, Ali, Herold, Thomas, and Baumann, Clemens

Published
2023
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9. Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations

Author

Qin, Chen, Shi, Bibo, Liao, Rui, Mansi, Tommaso, Rueckert, Daniel, and Kamen, Ali

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We propose a fully unsupervised multi-modal deformable image registration method (UMDIR), which does not require any ground truth deformation fields or any aligned multi-modal image pairs during training. Multi-modal registration is a key problem in many medical image analysis applications. It is very challenging due to complicated and unknown relationships between different modalities. In this paper, we propose an unsupervised learning approach to reduce the multi-modal registration problem to a mono-modal one through image disentangling. In particular, we decompose images of both modalities into a common latent shape space and separate latent appearance spaces via an unsupervised multi-modal image-to-image translation approach. The proposed registration approach is then built on the factorized latent shape code, with the assumption that the intrinsic shape deformation existing in original image domain is preserved in this latent space. Specifically, two metrics have been proposed for training the proposed network: a latent similarity metric defined in the common shape space and a learningbased image similarity metric based on an adversarial loss. We examined different variations of our proposed approach and compared them with conventional state-of-the-art multi-modal registration methods. Results show that our proposed methods achieve competitive performance against other methods at substantially reduced computation time., Comment: Accepted as an oral presentation in IPMI 2019

Published
2019

10. Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging

Author

Tuysuzoglu, Ahmet, Tan, Jeremy, Eissa, Kareem, Kiraly, Atilla P., Diallo, Mamadou, and Kamen, Ali

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Real-time localization of prostate gland in trans-rectal ultrasound images is a key technology that is required to automate the ultrasound guided prostate biopsy procedures. In this paper, we propose a new deep learning based approach which is aimed at localizing several prostate landmarks efficiently and robustly. We propose a multitask learning approach primarily to make the overall algorithm more contextually aware. In this approach, we not only consider the explicit learning of landmark locations, but also build-in a mechanism to learn the contour of the prostate. This multitask learning is further coupled with an adversarial arm to promote the generation of feasible structures. We have trained this network using ~4000 labeled trans-rectal ultrasound images and tested on an independent set of images with ground truth landmark locations. We have achieved an overall Dice score of 92.6% for the adversarially trained multitask approach, which is significantly better than the Dice score of 88.3% obtained by only learning of landmark locations. The overall mean distance error using the adversarial multitask approach has also improved by 20% while reducing the standard deviation of the error compared to learning landmark locations only. In terms of computational complexity both approaches can process the images in real-time using standard computer with a standard CUDA enabled GPU.

Published
2018

12. Deep Attentive Panoptic Model for Prostate Cancer Detection Using Biparametric MRI Scans

Author

Yu, Xin, Lou, Bin, Zhang, Donghao, Winkel, David, Arrahmane, Nacim, Diallo, Mamadou, Meng, Tongbai, von Busch, Heinrich, Grimm, Robert, Kiefer, Berthold, Comaniciu, Dorin, Kamen, Ali, 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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13. An Artificial Agent for Robust Image Registration

Author

Liao, Rui, Miao, Shun, de Tournemire, Pierre, Grbic, Sasa, Kamen, Ali, Mansi, Tommaso, and Comaniciu, Dorin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

3-D image registration, which involves aligning two or more images, is a critical step in a variety of medical applications from diagnosis to therapy. Image registration is commonly performed by optimizing an image matching metric as a cost function. However, this task is challenging due to the non-convex nature of the matching metric over the plausible registration parameter space and insufficient approaches for a robust optimization. As a result, current approaches are often customized to a specific problem and sensitive to image quality and artifacts. In this paper, we propose a completely different approach to image registration, inspired by how experts perform the task. We first cast the image registration problem as a "strategy learning" process, where the goal is to find the best sequence of motion actions (e.g. up, down, etc.) that yields image alignment. Within this approach, an artificial agent is learned, modeled using deep convolutional neural networks, with 3D raw image data as the input, and the next optimal action as the output. To cope with the dimensionality of the problem, we propose a greedy supervised approach for an end-to-end training, coupled with attention-driven hierarchical strategy. The resulting registration approach inherently encodes both a data-driven matching metric and an optimal registration strategy (policy). We demonstrate, on two 3-D/3-D medical image registration examples with drastically different nature of challenges, that the artificial agent outperforms several state-of-art registration methods by a large margin in terms of both accuracy and robustness., Comment: To appear in AAAI Conference 2017

Published
2016

16. Robust Non-rigid Registration Through Agent-Based Action Learning

Author

Krebs, Julian, Mansi, Tommaso, Delingette, Hervé, Zhang, Li, Ghesu, Florin C., Miao, Shun, Maier, Andreas K., Ayache, Nicholas, Liao, Rui, Kamen, Ali, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Descoteaux, Maxime, editor, Maier-Hein, Lena, editor, Franz, Alfred, editor, Jannin, Pierre, editor, Collins, D. Louis, editor, and Duchesne, Simon, editor

Published
2017
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17. Deep Convolutional Encoder-Decoders for Prostate Cancer Detection and Classification

Author

Kiraly, Atilla P., Nader, Clement Abi, Tuysuzoglu, Ahmet, Grimm, Robert, Kiefer, Berthold, El-Zehiry, Noha, Kamen, Ali, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Descoteaux, Maxime, editor, Maier-Hein, Lena, editor, Franz, Alfred, editor, Jannin, Pierre, editor, Collins, D. Louis, editor, and Duchesne, Simon, editor

Published
2017
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22. Towards an Efficient Computational Framework for Guiding Surgical Resection through Intra-operative Endo-microscopic Pathology

Author

Wan, Shaohua, Sun, Shanhui, Bhattacharya, Subhabrata, Kluckner, Stefan, Gigler, Alexander, Simon, Elfriede, Fleischer, Maximilian, Charalampaki, Patra, Chen, Terrence, Kamen, Ali, 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, Navab, Nassir, editor, Hornegger, Joachim, editor, Wells, William M., editor, and Frangi, Alejandro, editor

Published
2015
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23. Estimation of Regional Electrical Properties of the Heart from 12-Lead ECG and Images

Author

Seegerer, Philipp, Mansi, Tommaso, Jolly, Marie-Pierre, Neumann, Dominik, Georgescu, Bogdan, Kamen, Ali, Kayvanpour, Elham, Amr, Ali, Sedaghat-Hamedani, Farbod, Haas, Jan, Katus, Hugo, Meder, Benjamin, Comaniciu, Dorin, 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, Camara, Oscar, editor, Mansi, Tommaso, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2015
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25. A Framework for the Pre-clinical Validation of LBM-EP for the Planning and Guidance of Ventricular Tachycardia Ablation

Author

Mansi, Tommaso, Beinart, Roy, Zettinig, Oliver, Rapaka, Saikiran, Georgescu, Bogdan, Kamen, Ali, Dori, Yoav, Zviman, M. Muz, Herzka, Daniel A., Halperin, Henry R., Comaniciu, Dorin, 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, Camara, Oscar, editor, Mansi, Tommaso, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2014
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26. Model-Based Estimation of 4D Relative Pressure Map from 4D Flow MR Images

Author

Mihalef, Viorel, Rapaka, Saikiran, Gulsun, Mehmet, Scorza, Angelo, Sharma, Puneet, Itu, Lucian, Kamen, Ali, Barker, Alex, Markl, Michael, Comaniciu, Dorin, 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, Camara, Oscar, editor, Mansi, Tommaso, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2014
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27. Parameter Estimation for Personalization of Liver Tumor Radiofrequency Ablation

Author

Audigier, Chloé, Mansi, Tommaso, Delingette, Hervé, Rapaka, Saikiran, Mihalef, Viorel, Carnegie, Daniel, Boctor, Emad, Choti, Michael, Kamen, Ali, Comaniciu, Dorin, Ayache, Nicholas, 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, Yoshida, Hiroyuki, editor, Näppi, Janne J., editor, and Saini, Sanjay, editor

Published
2014
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28. Robust Image-Based Estimation of Cardiac Tissue Parameters and Their Uncertainty from Noisy Data

Author

Neumann, Dominik, Mansi, Tommaso, Georgescu, Bogdan, Kamen, Ali, Kayvanpour, Elham, Amr, Ali, Sedaghat-Hamedani, Farbod, Haas, Jan, Katus, Hugo, Meder, Benjamin, Hornegger, Joachim, Comaniciu, Dorin, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, 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, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Golland, Polina, editor, Hata, Nobuhiko, editor, Barillot, Christian, editor, Hornegger, Joachim, editor, and Howe, Robert, editor

Published
2014
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30. Comparison of a Deep Learning‐Accelerated vs. Conventional T2‐Weighted Sequence in Biparametric MRI of the Prostate.

Author

Tong, Angela, Bagga, Barun, Petrocelli, Robert, Smereka, Paul, Vij, Abhinav, Qian, Kun, Grimm, Robert, Kamen, Ali, Keerthivasan, Mahesh B, Nickel, Marcel Dominik, von Busch, Heinrich, and Chandarana, Hersh

Subjects
PROSTATE cancer, RECEIVER operating characteristic curves, PROSTATE, DIFFUSION magnetic resonance imaging, MAGNETIC resonance imaging, COMPUTER-aided diagnosis
Abstract

Background: Demand for prostate MRI is increasing, but scan times remain long even in abbreviated biparametric MRIs (bpMRI). Deep learning can be leveraged to accelerate T2‐weighted imaging (T2WI). Purpose: To compare conventional bpMRIs (CL‐bpMRI) with bpMRIs including a deep learning‐accelerated T2WI (DL‐bpMRI) in diagnosing prostate cancer. Study Type: Retrospective. Population: Eighty consecutive men, mean age 66 years (47–84) with suspected prostate cancer or prostate cancer on active surveillance who had a prostate MRI from December 28, 2020 to April 28, 2021 were included. Follow‐up included prostate biopsy or stability of prostate‐specific antigen (PSA) for 1 year. Field Strength and Sequences: A 3 T MRI. Conventional axial and coronal T2 turbo spin echo (CL‐T2), 3‐fold deep learning‐accelerated axial and coronal T2‐weighted sequence (DL‐T2), diffusion weighted imaging (DWI) with b = 50 sec/mm2, 1000 sec/mm2, calculated b = 1500 sec/mm2. Assessment: CL‐bpMRI and DL‐bpMRI including the same conventional diffusion‐weighted imaging (DWI) were presented to three radiologists (blinded to acquisition method) and to a deep learning computer‐assisted detection algorithm (DL‐CAD). The readers evaluated image quality using a 4‐point Likert scale (1 = nondiagnostic, 4 = excellent) and graded lesions using Prostate Imaging Reporting and Data System (PI‐RADS) v2.1. DL‐CAD identified and assigned lesions of PI‐RADS 3 or greater. Statistical Tests: Quality metrics were compared using Wilcoxon signed rank test, and area under the receiver operating characteristic curve (AUC) were compared using Delong's test. Significance: P = 0.05. Results: Eighty men were included (age: 66 ± 9 years; 17/80 clinically significant prostate cancer). Overall image quality results by the three readers (CL‐T2, DL‐T2) are reader 1: 3.72 ± 0.53, 3.89 ± 0.39 (P = 0.99); reader 2: 3.33 ± 0.82, 3.31 ± 0.74 (P = 0.49); reader 3: 3.67 ± 0.63, 3.51 ± 0.62. In the patient‐based analysis, the reader results of AUC are (CL‐bpMRI, DL‐bpMRI): reader 1: 0.77, 0.78 (P = 0.98), reader 2: 0.65, 0.66 (P = 0.99), reader 3: 0.57, 0.60 (P = 0.52). Diagnostic statistics from DL‐CAD (CL‐bpMRI, DL‐bpMRI) are sensitivity (0.71, 0.71, P = 1.00), specificity (0.59, 0.44, P = 0.05), positive predictive value (0.23, 0.24, P = 0.25), negative predictive value (0.88, 0.88, P = 0.48). Conclusion: Deep learning‐accelerated T2‐weighted imaging may potentially be used to decrease acquisition time for bpMRI. Evidence Level: 3. Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]

Published
2023
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35. Fast Data-Driven Calibration of a Cardiac Electrophysiology Model from Images and ECG

Author

Zettinig, Oliver, Mansi, Tommaso, Georgescu, Bogdan, Kayvanpour, Elham, Sedaghat-Hamedani, Farbod, Amr, Ali, Haas, Jan, Steen, Henning, Meder, Benjamin, Katus, Hugo, Navab, Nassir, Kamen, Ali, Comaniciu, Dorin, 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, Mori, Kensaku, editor, Sakuma, Ichiro, editor, Sato, Yoshinobu, editor, Barillot, Christian, editor, and Navab, Nassir, editor

Published
2013
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36. Lattice Boltzmann Method for Fast Patient-Specific Simulation of Liver Tumor Ablation from CT Images

Author

Audigier, Chloé, Mansi, Tommaso, Delingette, Hervé, Rapaka, Saikiran, Mihalef, Viorel, Sharma, Puneet, Carnegie, Daniel, Boctor, Emad, Choti, Michael, Kamen, Ali, Comaniciu, Dorin, Ayache, Nicholas, 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, Mori, Kensaku, editor, Sakuma, Ichiro, editor, Sato, Yoshinobu, editor, Barillot, Christian, editor, and Navab, Nassir, editor

Published
2013
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37. From Medical Images to Fast Computational Models of Heart Electromechanics: An Integrated Framework towards Clinical Use

Author

Zettinig, Oliver, Mansi, Tommaso, Georgescu, Bogdan, Rapaka, Saikiran, Kamen, Ali, Haas, Jan, Frese, Karen S., Sedaghat-Hamedani, Farbod, Kayvanpour, Elham, Amr, Ali, Hardt, Stefan, Mereles, Derliz, Steen, Henning, Keller, Andreas, Katus, Hugo A., Meder, Benjamin, Navab, Nassir, Comaniciu, Dorin, 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, Ourselin, Sébastien, editor, Rueckert, Daniel, editor, and Smith, Nicolas, editor

Published
2013
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38. Data-Driven Reduction of a Cardiac Myofilament Model

Author

Mansi, Tommaso, Georgescu, Bogdan, Hussan, Jagir, Hunter, Peter J., Kamen, Ali, Comaniciu, Dorin, 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, Ourselin, Sébastien, editor, Rueckert, Daniel, editor, and Smith, Nicolas, editor

Published
2013
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40. Facilitating 3D Spectroscopic Imaging through Automatic Prostate Localization in MR Images Using Random Walker Segmentation Initialized via Boosted Classifiers

Author

Khurd, Parmeshwar, Grady, Leo, Gajera, Kalpitkumar, Diallo, Mamadou, Gall, Peter, Requardt, Martin, Kiefer, Berthold, Weiss, Clifford, Kamen, Ali, 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, Madabhushi, Anant, editor, Dowling, Jason, editor, Huisman, Henkjan, editor, and Barratt, Dean, editor

Published
2011
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41. Imaging as a Surrogate for the Early Prediction and Assessment of Treatment Response through the Analysis of 4-D Texture Ensembles (ISEPARATE)

Author

Maday, Peter, Khurd, Parmeshwar, Ladic, Lance, Schnall, Mitchell, Rosen, Mark, Davatzikos, Christos, Kamen, Ali, 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, Menze, Bjoern, editor, Langs, Georg, editor, Tu, Zhuowen, editor, and Criminisi, Antonio, editor

Published
2011
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43. Generalization of Deformable Registration in Riemannian Sobolev Spaces

Author

Zikic, Darko, Baust, Maximilian, Kamen, Ali, Navab, Nassir, 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, Jiang, Tianzi, editor, Navab, Nassir, editor, Pluim, Josien P. W., editor, and Viergever, Max A., editor

Published
2010
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44. Recursive Green’s Function Registration

Author

Beuthien, Björn, Kamen, Ali, Fischer, Bernd, 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, Jiang, Tianzi, editor, Navab, Nassir, editor, Pluim, Josien P. W., editor, and Viergever, Max A., editor

Published
2010
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45. An Efficient Graph-Based Deformable 2D/3D Registration Algorithm with Applications for Abdominal Aortic Aneurysm Interventions

Author

Liao, Rui, Tan, Yunhao, Sundar, Hari, Pfister, Marcus, Kamen, Ali, 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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46. A Learning-Based Approach to Evaluate Registration Success

Author

Vetter, Christoph, Kamen, Ali, Khurd, Parmeshwar, Westermann, Rüdiger, 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. Unifying Characterization of Deformable Registration Methods Based on the Inherent Parametrization : An Attempt at an Alternative Analysis Approach

Author

Zikic, Darko, Kamen, Ali, Navab, Nassir, 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, Fischer, Bernd, editor, Dawant, Benoît M., editor, and Lorenz, Cristian, editor

Published
2010
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50. A concurrent, deep learning–based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists

Author

Labus, Sandra, primary, Altmann, Martin M., additional, Huisman, Henkjan, additional, Tong, Angela, additional, Penzkofer, Tobias, additional, Choi, Moon Hyung, additional, Shabunin, Ivan, additional, Winkel, David J., additional, Xing, Pengyi, additional, Szolar, Dieter H., additional, Shea, Steven M., additional, Grimm, Robert, additional, von Busch, Heinrich, additional, Kamen, Ali, additional, Herold, Thomas, additional, and Baumann, Clemens, additional

Published
2022
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