no code implementations • 8 Mar 2024 • Qingqing Zhu, Benjamin Hou, Tejas S. Mathai, Pritam Mukherjee, Qiao Jin, Xiuying Chen, Zhizheng Wang, Ruida Cheng, Ronald M. Summers, Zhiyong Lu
The volume of CT exams being done in the world has been rising every year, which has led to radiologist burn-out.
no code implementations • 6 Mar 2024 • Jianfei Liu, Christopher Parnell, Ronald M. Summers
Validation results on 11 patients showed that the accuracy of training labels was statistically significantly improved, with the Dice similarity coefficient of muscle, subcutaneous and visceral adipose tissue increased from 74. 2% to 91. 5%, 91. 2% to 95. 6%, and 77. 6% to 88. 5%, respectively (p<0. 05).
no code implementations • 14 Feb 2024 • Andrew M. Nguyen, Jianfei Liu, Tejas Sudharshan Mathai, Peter C. Grayson, Ronald M. Summers
Heart, aorta, and lung segmentations were determined using TotalSegmentator, while plaques in the coronary arteries and heart valves were manually labeled for 801 volumes.
no code implementations • 12 Feb 2024 • Kimberly Helm, Tejas Sudharshan Mathai, Boah Kim, Pritam Mukherjee, Jianfei Liu, Ronald M. Summers
In order to reduce clinician oversight and ensure the validity of the DICOM headers, we propose an automated method to classify the 3D MRI sequence acquired at the levels of the chest, abdomen, and pelvis.
no code implementations • 12 Feb 2024 • David C. Oluigboa, Bikash Santra, Tejas Sudharshan Mathai, Pritam Mukherjee, Jianfei Liu, Abhishek Jha, Mayank Patel, Karel Pacak, Ronald M. Summers
Pheochromocytomas and Paragangliomas (PPGLs) are rare adrenal and extra-adrenal tumors which have the potential to metastasize.
no code implementations • 31 Jan 2024 • Tao Sheng, Tejas Sudharshan Mathai, Alexander Shieh, Ronald M. Summers
First, we used the bone lesions that were prospectively marked by radiologists in a few 2D slices of CT volumes and converted them into weak 3D segmentation masks.
no code implementations • 16 Jan 2024 • Qiao Jin, Fangyuan Chen, Yiliang Zhou, Ziyang Xu, Justin M. Cheung, Robert Chen, Ronald M. Summers, Justin F. Rousseau, Peiyun Ni, Marc J Landsman, Sally L. Baxter, Subhi J. Al'Aref, Yijia Li, Michael F. Chiang, Yifan Peng, Zhiyong Lu
Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks.
no code implementations • 11 Jan 2024 • Tejas Sudharshan Mathai, Bohan Liu, Ronald M. Summers
Purpose: Lymph nodes (LNs) in the chest have a tendency to enlarge due to various pathologies, such as lung cancer or pneumonia.
no code implementations • 10 Jan 2024 • Benjamin Hou, Tejas Sudharshan Mathai, Jianfei Liu, Christopher Parnell, Ronald M. Summers
This study evaluates the reliability of an Internal tool for the segmentation of muscle and fat (subcutaneous and visceral) as compared to the well-established public TotalSegmentator tool.
no code implementations • 11 Dec 2023 • Yan Zhuang, Benjamin Hou, Tejas Sudharshan Mathai, Pritam Mukherjee, Boah Kim, Ronald M. Summers
As a new emerging and promising type of generative models, diffusion models have proven to outperform Generative Adversarial Networks (GANs) in multiple tasks, including image synthesis.
no code implementations • 24 Nov 2023 • M. Jorge Cardoso, Julia Moosbauer, Tessa S. Cook, B. Selnur Erdal, Brad Genereaux, Vikash Gupta, Bennett A. Landman, Tiarna Lee, Parashkev Nachev, Elanchezhian Somasundaram, Ronald M. Summers, Khaled Younis, Sebastien Ourselin, Franz MJ Pfister
The integration of AI into radiology introduces opportunities for improved clinical care provision and efficiency but it demands a meticulous approach to mitigate potential risks as with any other new technology.
no code implementations • 22 Nov 2023 • Andrew M. Nguyen, Tejas Sudharshan Mathai, Liangchen Liu, Jianfei Liu, Ronald M. Summers
In this pilot work, we developed a fully automated approach for the measurement of PCAT mean attenuation and volume in the region around both coronary arteries.
no code implementations • 24 Oct 2023 • Gregory Holste, Yiliang Zhou, Song Wang, Ajay Jaiswal, Mingquan Lin, Sherry Zhuge, Yuzhe Yang, Dongkyun Kim, Trong-Hieu Nguyen-Mau, Minh-Triet Tran, Jaehyup Jeong, Wongi Park, Jongbin Ryu, Feng Hong, Arsh Verma, Yosuke Yamagishi, Changhyun Kim, Hyeryeong Seo, Myungjoo Kang, Leo Anthony Celi, Zhiyong Lu, Ronald M. Summers, George Shih, Zhangyang Wang, Yifan Peng
Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more relatively rare conditions.
no code implementations • 6 Sep 2023 • Mengliang Zhang, Xinyue Hu, Lin Gu, Liangchen Liu, Kazuma Kobayashi, Tatsuya Harada, Ronald M. Summers, Yingying Zhu
In this paper, we re-extract disease labels from CXR reports to make them more realistic by considering disease severity and uncertainty in classification.
1 code implementation • 17 Aug 2023 • Gregory Holste, Ziyu Jiang, Ajay Jaiswal, Maria Hanna, Shlomo Minkowitz, Alan C. Legasto, Joanna G. Escalon, Sharon Steinberger, Mark Bittman, Thomas C. Shen, Ying Ding, Ronald M. Summers, George Shih, Yifan Peng, Zhangyang Wang
This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification.
no code implementations • 31 Jul 2023 • Boah Kim, Yujin Oh, Bradford J. Wood, Ronald M. Summers, Jong Chul Ye
Blood vessel segmentation in medical imaging is one of the essential steps for vascular disease diagnosis and interventional planning in a broad spectrum of clinical scenarios in image-based medicine and interventional medicine.
1 code implementation • 22 Jul 2023 • Xinyue Hu, Lin Gu, Qiyuan An, Mengliang Zhang, Liangchen Liu, Kazuma Kobayashi, Tatsuya Harada, Ronald M. Summers, Yingying Zhu
Given a pair of main and reference images, this task attempts to answer several questions on both diseases and, more importantly, the differences between them.
1 code implementation • 11 Jul 2023 • Seung Yeon Shin, Thomas C. Shen, Ronald M. Summers
We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks.
1 code implementation • 14 Jun 2023 • Qingqing Zhu, Tejas Sudharshan Mathai, Pritam Mukherjee, Yifan Peng, Ronald M. Summers, Zhiyong Lu
Pre-filling a radiology report holds promise in mitigating reporting errors, and despite efforts in the literature to generate medical reports, there exists a lack of approaches that exploit the longitudinal nature of patient visit records in the MIMIC-CXR dataset.
no code implementations • 3 Feb 2023 • Annika Reinke, Minu D. Tizabi, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, A. Emre Kavur, Tim Rädsch, Carole H. Sudre, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew Blaschko, Florian Buettner, M. Jorge Cardoso, Veronika Cheplygina, Jianxu Chen, Evangelia Christodoulou, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Jens Kleesiek, Florian Kofler, Thijs Kooi, Annette Kopp-Schneider, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Susanne M. Rafelski, Nasir Rajpoot, Mauricio Reyes, Michael A. Riegler, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul F. Jäger, Lena Maier-Hein
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
1 code implementation • 29 Aug 2022 • Gregory Holste, Song Wang, Ziyu Jiang, Thomas C. Shen, George Shih, Ronald M. Summers, Yifan Peng, Zhangyang Wang
Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings.
Ranked #1 on Long-tail Learning on MIMIC-CXR-LT
no code implementations • 29 Jul 2022 • Seung Yeon Shin, SungWon Lee, Ronald M. Summers
To circumvent this, a series of cylinders that are fitted along the course of the small bowel are used to guide the tracking to more reliable directions.
no code implementations • 29 Jul 2022 • Seung Yeon Shin, Thomas C. Shen, Stephen A. Wank, Ronald M. Summers
Our method can be one option for explicitly incorporating intensity distribution information of a target in network training.
no code implementations • 29 Jun 2022 • Seung Yeon Shin, Ronald M. Summers
The proposed method holds a high degree of usability in this problem by being able to utilize the scans with weak annotations, and thus by possibly reducing the required annotation cost.
1 code implementation • 3 Jun 2022 • Lena Maier-Hein, Annika Reinke, Patrick Godau, Minu D. Tizabi, Florian Buettner, Evangelia Christodoulou, Ben Glocker, Fabian Isensee, Jens Kleesiek, Michal Kozubek, Mauricio Reyes, Michael A. Riegler, Manuel Wiesenfarth, A. Emre Kavur, Carole H. Sudre, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, Tim Rädsch, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew Blaschko, M. Jorge Cardoso, Veronika Cheplygina, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Florian Kofler, Annette Kopp-Schneider, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Nasir Rajpoot, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Paul F. Jäger
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
no code implementations • 31 Mar 2022 • Tejas Sudharshan Mathai, SungWon Lee, Thomas C. Shen, Zhiyong Lu, Ronald M. Summers
Results: Experiments on 122 test T2 MRI volumes revealed that VFNet achieved a 51. 1% mAP and 78. 7% recall at 4 false positives (FP) per volume, while the one-stage model ensemble achieved a mAP of 52. 3% and sensitivity of 78. 7% at 4FP.
no code implementations • 9 Nov 2021 • Tejas Sudharshan Mathai, SungWon Lee, Daniel C. Elton, Thomas C. Shen, Yifan Peng, Zhiyong Lu, Ronald M. Summers
Identification of lymph nodes (LN) in T2 Magnetic Resonance Imaging (MRI) is an important step performed by radiologists during the assessment of lymphoproliferative diseases.
no code implementations • 9 Nov 2021 • Tarun Mattikalli, Tejas Sudharshan Mathai, Ronald M. Summers
In clinical practice, radiologists are reliant on the lesion size when distinguishing metastatic from non-metastatic lesions.
no code implementations • 1 Oct 2021 • Seung Yeon Shin, SungWon Lee, Ronald M. Summers
It is formulated as finding the minimum cost path between given start and end nodes on a graph that is constructed based on the bowel wall detection.
no code implementations • 6 Jul 2021 • Seung Yeon Shin, SungWon Lee, Ronald M. Summers
We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement.
1 code implementation • 10 Jun 2021 • Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, AnnetteKopp-Schneider, Bennett A. Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M. Summers, Bram van Ginneken, Michel Bilello, Patrick Bilic, Patrick F. Christ, Richard K. G. Do, Marc J. Gollub, Stephan H. Heckers, William R. Jarnagin, Maureen K. McHugo, Sandy Napel, Jennifer S. Goli Pernicka, Kawal Rhode, Catalina Tobon-Gomez, Eugene Vorontsov, Henkjan Huisman, James A. Meakin, Sebastien Ourselin, Manuel Wiesenfarth, Pablo Arbelaez, Byeonguk Bae, Sihong Chen, Laura Daza, Jianjiang Feng, Baochun He, Fabian Isensee, Yuanfeng Ji, Fucang Jia, Namkug Kim, Ildoo Kim, Dorit Merhof, Akshay Pai, Beomhee Park, Mathias Perslev, Ramin Rezaiifar, Oliver Rippel, Ignacio Sarasua, Wei Shen, Jaemin Son, Christian Wachinger, Liansheng Wang, Yan Wang, Yingda Xia, Daguang Xu, Zhanwei Xu, Yefeng Zheng, Amber L. Simpson, Lena Maier-Hein, M. Jorge Cardoso
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
1 code implementation • 12 Apr 2021 • Annika Reinke, Minu D. Tizabi, Carole H. Sudre, Matthias Eisenmann, Tim Rädsch, Michael Baumgartner, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, Matthew Blaschko, Florian Buettner, M. Jorge Cardoso, Jianxu Chen, Veronika Cheplygina, Evangelia Christodoulou, Beth Cimini, Gary S. Collins, Sandy Engelhardt, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Fred Hamprecht, Daniel A. Hashimoto, Doreen Heckmann-Nötzel, Peter Hirsch, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, A. Emre Kavur, Hannes Kenngott, Jens Kleesiek, Andreas Kleppe, Sven Kohler, Florian Kofler, Annette Kopp-Schneider, Thijs Kooi, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, David Moher, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, M. Alican Noyan, Jens Petersen, Gorkem Polat, Susanne M. Rafelski, Nasir Rajpoot, Mauricio Reyes, Nicola Rieke, Michael Riegler, Hassan Rivaz, Julio Saez-Rodriguez, Clara I. Sánchez, Julien Schroeter, Anindo Saha, M. Alper Selver, Lalith Sharan, Shravya Shetty, Maarten van Smeden, Bram Stieltjes, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul Jäger, Lena Maier-Hein
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
no code implementations • 2 Aug 2020 • S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers
In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues.
no code implementations • 19 Jul 2020 • Youbao Tang, Yu-Xing Tang, Yingying Zhu, Jing Xiao, Ronald M. Summers
We introduce an edge prediction module in E$^2$Net and design an edge distance map between liver and tumor boundaries, which is used as an extra supervision signal to train the edge enhanced network.
no code implementations • 16 Jul 2020 • Seung Yeon Shin, Sung-Won Lee, Daniel C. Elton, James L. Gulley, Ronald M. Summers
Since the inner cylinder is free of the touching issue, a cylindrical shape constraint applied on this augmented branch guides the network to generate a topologically correct segmentation.
no code implementations • 14 Jul 2020 • Yingying Zhu, You-Bao Tang, Yu-Xing Tang, Daniel C. Elton, Sung-Won Lee, Perry J. Pickhardt, Ronald M. Summers
We expect the utility of our framework will extend to other problems beyond segmentation due to the improved quality of the generated images and enhanced ability to preserve small structures.
no code implementations • 12 Jul 2020 • Chen Qin, Jo Schlemper, Kerstin Hammernik, Jinming Duan, Ronald M. Summers, Daniel Rueckert
We present a deep network interpolation strategy for accelerated parallel MR image reconstruction.
1 code implementation • 11 Jun 2020 • Yifan Peng, Yu-Xing Tang, Sung-Won Lee, Yingying Zhu, Ronald M. Summers, Zhiyong Lu
(1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We trained an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR.
no code implementations • 22 May 2020 • Yingying Zhu, Daniel C. Elton, SungWon Lee, Perry J. Pickhardt, Ronald M. Summers
In medical imaging applications, preserving small structures is important since these structures can carry information which is highly relevant for disease diagnosis.
no code implementations • 18 Mar 2020 • Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett Landman, Klaus Maier-Hein, Sebastien Ourselin, Micah Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso
Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems.
no code implementations • 8 Feb 2020 • Jia Liang, Yu-Xing Tang, You-Bao Tang, Jing Xiao, Ronald M. Summers
Dual-energy (DE) chest radiography provides the capability of selectively imaging two clinically relevant materials, namely soft tissues, and osseous structures, to better characterize a wide variety of thoracic pathology and potentially improve diagnosis in posteroanterior (PA) chest radiographs.
no code implementations • 28 Jan 2020 • Daniel C. Elton, Veit Sandfort, Perry J. Pickhardt, Ronald M. Summers
We next developed an algorithm which performs iterative instance segmentation and classification of the entire spine with a 3D U-Net.
no code implementations • MIDL 2019 • Yingying Zhu, Daniel C. Elton, SungWon Lee, Perry J. Pickhardt, Ronald M. Summers
In medical imaging applications, preserving small structures is important since these structures can carry information which is highly relevant for disease diagnosis.
no code implementations • 24 Jan 2020 • Vatsal Agarwal, You-Bao Tang, Jing Xiao, Ronald M. Summers
In this work, we propose a weakly-supervised co-segmentation model that first generates pseudo-masks from the RECIST slices and uses these as training labels for an attention-based convolutional neural network capable of segmenting common lesions from a pair of CT scans.
no code implementations • 23 Jan 2020 • Vatsal Agarwal, You-Bao Tang, Jing Xiao, Ronald M. Summers
Lesion segmentation on computed tomography (CT) scans is an important step for precisely monitoring changes in lesion/tumor growth.
1 code implementation • 18 Dec 2019 • Kerstin Hammernik, Jo Schlemper, Chen Qin, Jinming Duan, Ronald M. Summers, Daniel Rueckert
Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a $\Sigma$-net, for accelerated parallel MR image reconstruction using deep learning.
1 code implementation • 11 Dec 2019 • Jo Schlemper, Chen Qin, Jinming Duan, Ronald M. Summers, Kerstin Hammernik
We explore an ensembled $\Sigma$-net for fast parallel MR imaging, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit sensitivity maps.
no code implementations • 21 Aug 2019 • Yu-Xing Tang, You-Bao Tang, Veit Sandfort, Jing Xiao, Ronald M. Summers
In this work, we exploit the unsupervised domain adaptation problem for radiology image interpretation across domains.
Generative Adversarial Network Image-to-Image Translation +2
14 code implementations • 12 Aug 2019 • Ke Yan, You-Bao Tang, Yifan Peng, Veit Sandfort, Mohammadhadi Bagheri, Zhiyong Lu, Ronald M. Summers
When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report.
Ranked #7 on Medical Object Detection on DeepLesion
no code implementations • 30 Apr 2019 • Yifan Peng, Ke Yan, Veit Sandfort, Ronald M. Summers, Zhiyong Lu
In radiology, radiologists not only detect lesions from the medical image, but also describe them with various attributes such as their type, location, size, shape, and intensity.
3 code implementations • 19 Apr 2019 • Youbao Tang, Yu-Xing Tang, Jing Xiao, Ronald M. Summers
To reduce the manual annotation burden and to train a robust lung segmentor that can be adapted to pathological lungs with hazy lung boundaries, an image-to-image translation module is employed to synthesize radiorealistic abnormal CXRs from the source of normal ones for data augmentation.
Ranked #1 on Lung Nodule Segmentation on NIH
3 code implementations • CVPR 2019 • Ke Yan, Yifan Peng, Veit Sandfort, Mohammadhadi Bagheri, Zhiyong Lu, Ronald M. Summers
In radiologists' routine work, one major task is to read a medical image, e. g., a CT scan, find significant lesions, and describe them in the radiology report.
no code implementations • 5 Mar 2019 • Yu-Xing Tang, You-Bao Tang, Mei Han, Jing Xiao, Ronald M. Summers
Given a chest X-ray image in the testing phase, if it is normal, the learned architecture can well model and reconstruct the content; if it is abnormal, since the content is unseen in the training phase, the model would perform poorly in its reconstruction.
no code implementations • 4 Mar 2019 • Ke Yan, Yifan Peng, Zhiyong Lu, Ronald M. Summers
To address this problem, we define a set of 145 labels based on RadLex to describe a large variety of lesions in the DeepLesion dataset.
11 code implementations • 25 Feb 2019 • Amber L. Simpson, Michela Antonelli, Spyridon Bakas, Michel Bilello, Keyvan Farahani, Bram van Ginneken, Annette Kopp-Schneider, Bennett A. Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M. Summers, Patrick Bilic, Patrick F. Christ, Richard K. G. Do, Marc Gollub, Jennifer Golia-Pernicka, Stephan H. Heckers, William R. Jarnagin, Maureen K. McHugo, Sandy Napel, Eugene Vorontsov, Lena Maier-Hein, M. Jorge Cardoso
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization.
no code implementations • 23 Feb 2019 • Ling Zhang, Le Lu, Xiaosong Wang, Robert M. Zhu, Mohammadhadi Bagheri, Ronald M. Summers, Jianhua Yao
Results validate that the ST-ConvLSTM produces a Dice score of 83. 2%+-5. 1% and a RVD of 11. 2%+-10. 8%, both significantly outperforming (p<0. 05) other compared methods of linear model, ConvLSTM, and generative adversarial network (GAN) under the metric of predicting future tumor volumes.
1 code implementation • 18 Jan 2019 • Youbao Tang, Ke Yan, Yu-Xing Tang, Jiamin Liu, Jing Xiao, Ronald M. Summers
To address this problem, this work constructs a pseudo mask for each lesion region that can be considered as a surrogate of the real mask, based on which the Mask R-CNN is employed for lesion detection.
no code implementations • 19 Jul 2018 • Yu-Xing Tang, Xiaosong Wang, Adam P. Harrison, Le Lu, Jing Xiao, Ronald M. Summers
In addition, highly confident samples (measured by classification probabilities) and their corresponding class-conditional heatmaps (generated by the CNN) are extracted and further fed into the AGCL framework to guide the learning of more distinctive convolutional features in the next iteration.
no code implementations • 18 Jul 2018 • Youbao Tang, Jinzheng Cai, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers
The first GAN reduces the noise in the CT image and the second GAN generates a higher resolution image with enhanced boundaries and high contrast.
no code implementations • 2 Jul 2018 • Jinzheng Cai, You-Bao Tang, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers
Volumetric lesion segmentation from computed tomography (CT) images is a powerful means to precisely assess multiple time-point lesion/tumor changes.
4 code implementations • 25 Jun 2018 • Ke Yan, Mohammadhadi Bagheri, Ronald M. Summers
3D context is known to be helpful in this differentiation task.
Ranked #10 on Medical Object Detection on DeepLesion
no code implementations • 25 Jun 2018 • Youbao Tang, Adam P. Harrison, Mohammadhadi Bagheri, Jing Xiao, Ronald M. Summers
Response evaluation criteria in solid tumors (RECIST) is the standard measurement for tumor extent to evaluate treatment responses in cancer patients.
no code implementations • 25 Jan 2018 • Ling Zhang, Le Lu, Ronald M. Summers, Electron Kebebew, Jianhua Yao
Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics.
no code implementations • 25 Jan 2018 • Jinzheng Cai, You-Bao Tang, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers
Toward this end, we introduce a convolutional neural network based weakly supervised self-paced segmentation (WSSS) method to 1) generate the initial lesion segmentation on the axial RECIST-slice; 2) learn the data distribution on RECIST-slices; 3) adapt to segment the whole volume slice by slice to finally obtain a volumetric segmentation.
no code implementations • 25 Jan 2018 • Ling Zhang, Vissagan Gopalakrishnan, Le Lu, Ronald M. Summers, Joel Moss, Jianhua Yao
In recent years, deep neural networks achieve impressive performances on many medical image segmentation tasks by supervised learning on large manually annotated data.
no code implementations • 25 Jan 2018 • Ling Zhang, Le Lu, Isabella Nogues, Ronald M. Summers, Shaoxiong Liu, Jianhua Yao
However, the success of most traditional classification methods relies on the presence of accurate cell segmentations.
no code implementations • 25 Jan 2018 • Zhihui Guo, Ling Zhang, Le Lu, Mohammadhadi Bagheri, Ronald M. Summers, Milan Sonka, Jianhua Yao
The cost for each node of the graph is determined by the UNet probability maps.
no code implementations • CVPR 2018 • Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Ronald M. Summers
Chest X-rays are one of the most common radiological examinations in daily clinical routines.
1 code implementation • 4 Oct 2017 • Ke Yan, Xiaosong Wang, Le Lu, Ronald M. Summers
We categorize the collection of lesions using an unsupervised deep mining scheme to generate clustered pseudo lesion labels.
2 code implementations • 12 Jul 2017 • Ke Yan, Le Lu, Ronald M. Summers
In this paper, we propose a convolutional neural network (CNN) based Unsupervised Body part Regression (UBR) algorithm to address this problem.
no code implementations • 12 Jun 2017 • Adam P. Harrison, Ziyue Xu, Kevin George, Le Lu, Ronald M. Summers, Daniel J. Mollura
Pathological lung segmentation (PLS) is an important, yet challenging, medical image application due to the wide variability of pathological lung appearance and shape.
no code implementations • 1 Jun 2017 • Ling Zhang, Le Lu, Ronald M. Summers, Electron Kebebew, Jianhua Yao
Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient's tumor.
25 code implementations • CVPR 2017 • Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, Ronald M. Summers
The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases.
no code implementations • 31 Jan 2017 • Holger R. Roth, Le Lu, Nathan Lay, Adam P. Harrison, Amal Farag, Andrew Sohn, Ronald M. Summers
Accurate and automatic organ segmentation from 3D radiological scans is an important yet challenging problem for medical image analysis.
Ranked #1 on 3D Medical Imaging Segmentation on TCIA Pancreas-CT
no code implementations • 23 Jan 2017 • Xiaosong Wang, Le Lu, Hoo-chang Shin, Lauren Kim, Mohammadhadi Bagheri, Isabella Nogues, Jianhua Yao, Ronald M. Summers
The recent rapid and tremendous success of deep convolutional neural networks (CNN) on many challenging computer vision tasks largely derives from the accessibility of the well-annotated ImageNet and PASCAL VOC datasets.
no code implementations • 19 Jan 2017 • Mingchen Gao, Ziyue Xu, Le Lu, Adam P. Harrison, Ronald M. Summers, Daniel J. Mollura
Accurately predicting and detecting interstitial lung disease (ILD) patterns given any computed tomography (CT) slice without any pre-processing prerequisites, such as manually delineated regions of interest (ROIs), is a clinically desirable, yet challenging goal.
no code implementations • 24 Jun 2016 • Holger R. Roth, Le Lu, Amal Farag, Andrew Sohn, Ronald M. Summers
Accurate automatic organ segmentation is an important yet challenging problem for medical image analysis.
1 code implementation • CVPR 2016 • Hoo-chang Shin, Kirk Roberts, Le Lu, Dina Demner-Fushman, Jianhua Yao, Ronald M. Summers
Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features.
no code implementations • 10 Feb 2016 • Hoo-chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, Ronald M. Summers
Another effective method is transfer learning, i. e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks.
no code implementations • 1 Feb 2016 • Yinong Wang, Jianhua Yao, Holger R. Roth, Joseph E. Burns, Ronald M. Summers
The use of joint vertebra-rib atlases produced a statistically significant increase in the Dice coefficient from 92. 5 $\pm$ 3. 1% to 93. 8 $\pm$ 2. 1% for the left and right transverse processes and a decrease in the mean and max surface distance from 0. 75 $\pm$ 0. 60mm and 8. 63 $\pm$ 4. 44mm to 0. 30 $\pm$ 0. 27mm and 3. 65 $\pm$ 2. 87mm, respectively.
no code implementations • 29 Jan 2016 • Holger R. Roth, Yinong Wang, Jianhua Yao, Le Lu, Joseph E. Burns, Ronald M. Summers
In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine.
no code implementations • 27 Jan 2016 • Yinong Wang, Jianhua Yao, Joseph E. Burns, Ronald M. Summers
Classification of vertebral compression fractures (VCF) having osteoporotic or neoplastic origin is fundamental to the planning of treatment.
no code implementations • 13 Jan 2016 • Yinong Wang, Jianhua Yao, Holger R. Roth, Joseph E. Burns, Ronald M. Summers
The precise and accurate segmentation of the vertebral column is essential in the diagnosis and treatment of various orthopedic, neurological, and oncological traumas and pathologies.
no code implementations • 22 Jun 2015 • Holger R. Roth, Le Lu, Amal Farag, Hoo-chang Shin, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i. e. superpixels.
no code implementations • CVPR 2015 • Hoo-chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers
We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's picture archiving and communication system.
no code implementations • 22 May 2015 • Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading superpixels.
no code implementations • 12 May 2015 • Holger R. Roth, Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin Cherry, Lauren Kim, Ronald M. Summers
By leveraging existing CAD systems, coordinates of regions or volumes of interest (ROI or VOI) for lesion candidates are generated in this step and function as input for a second tier, which is our focus in this study.
no code implementations • 4 May 2015 • Hoo-chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers
We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's Picture Archiving and Communication System.
1 code implementation • 15 Apr 2015 • Holger R. Roth, Christopher T. Lee, Hoo-chang Shin, Ari Seff, Lauren Kim, Jianhua Yao, Le Lu, Ronald M. Summers
We show that a data augmentation approach can help to enrich the data set and improve classification performance.
no code implementations • 15 Apr 2015 • Holger R. Roth, Amal Farag, Le Lu, Evrim B. Turkbey, Ronald M. Summers
We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing).
no code implementations • 14 Aug 2014 • Ari Seff, Le Lu, Kevin M. Cherry, Holger Roth, Jiamin Liu, Shijun Wang, Joanne Hoffman, Evrim B. Turkbey, Ronald M. Summers
In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue.
no code implementations • 31 Jul 2014 • Amal Farag, Le Lu, Evrim Turkbey, Jiamin Liu, Ronald M. Summers
Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD) system to detect pathologies and perform quantitative analysis.
no code implementations • 22 Jul 2014 • Holger R. Roth, Jianhua Yao, Le Lu, James Stieger, Joseph E. Burns, Ronald M. Summers
In testing, the CNN is employed to assign individual probabilities for a new set of N random views that are averaged at each ROI to compute a final per-candidate classification probability.
no code implementations • 6 Jun 2014 • Holger R. Roth, Le Lu, Ari Seff, Kevin M. Cherry, Joanne Hoffman, Shijun Wang, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
and 84%/90% at 6 FP/vol.