VLDB 2026 Research / reviewers in the wild / expert
Jun Ma 0016
dblp:91/4845-16
· DBLP profile ↗
20ranked-venue papers
6as first author
19since 2021 · last 2026
0000-0002-9739-0855ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 15 · 4 first-author · 14 since 2021Artificial intelligence and machine learning · 4 · 1 first-author · 4 since 2021Graphics, computer vision, multimedia, augmented reality and games · 4 · 2 first-author · 4 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | IUGC: A benchmark of landmark detection in end-to-end intrapartum ultrasound biometry
Jieyun Bai, Yitong Tang, Xiao Liu 0037, Jiale Hu, Yunda Li, Xufan Chen, Yunshu Li, Bowen Guo, Jing Jiao, Lifei Li, Yuzhang Ma, Xiaoxin Han, Haochen Shao, Qingchen Liu, Jingfan Kuang, Shanglin Song, Anirvan Krishna, Zaid Ahmed Khan, Zelan Li, Zhengyang Zhang, Hansen Zhang, Xuezhi Zhang, Lyuyang Tong, Bo Du 0004, Yu Chen 0099, Zilun Peng, Saeid Rezaei, Tom Weidong Cai, Fangyijie Wang, Kathleen M. Curran, Guénolé C. M. Silvestre, Isaac Khobo, Yaosheng Lu, Dong Ni 0001, Mohammad Yaqub, Jun Ma 0016, Karim Lekadir, Shuo Li 0001 |
Medical Image Anal. | 48 |
| 2025 | MorphoDiff: Cellular Morphology Painting with Diffusion ModelsabstractUnderstanding cellular responses to external stimuli is critical for parsing biological mechanisms and advancing therapeutic development. High-content image-based assays provide a cost-effective approach to examine cellular phenotypes induced by diverse interventions, which offers valuable insights into biological processes and cellular states. We introduce MorphoDiff, a generative pipeline to predict high-resolution cell morphological responses under different conditions based on perturbation encoding. To the best of our knowledge, MorphoDiff is the first framework capable of producing guided, high-resolution predictions of cell morphology that generalize across both chemical and genetic interventions. The model integrates perturbation embeddings as guiding signals within a 2D latent diffusion model. The comprehensive computational, biological, and visual validations across three open-source Cell Painting datasets show that MorphoDiff can generate high-fidelity images and produce meaningful biology signals under various interventions. We envision the model will facilitate efficient in silico exploration of perturbational landscapes towards more effective drug discovery studies. Zeinab Navidi, Jun Ma 0016, Esteban Miglietta, Anne E. Carpenter, Beth A. Cimini, Benjamin Haibe-Kains, Bo Wang 0044 |
ICLR | 2 |
| 2025 | MedRAX: Medical Reasoning Agent for Chest X-rayabstractChest X-rays (CXRs) play an integral role in driving critical decisions in disease management and patient care. While recent innovations have led to specialized models for various CXR interpretation tasks, these solutions often operate in isolation, limiting their practical utility in clinical practice. We present MedRAX, the first versatile AI agent that seamlessly integrates state-of-the-art CXR analysis tools and multimodal large language models into a unified framework. MedRAX dynamically leverages these models to address complex medical queries without requiring additional training. To rigorously evaluate its capabilities, we introduce ChestAgentBench, a comprehensive benchmark containing 2,500 complex medical queries across 7 diverse categories. Our experiments demonstrate that MedRAX achieves state-of-the-art performance compared to both open-source and proprietary models, representing a significant step toward the practical deployment of automated CXR interpretation systems. Data and code have been publicly available at https://github.com/bowang-lab/MedRAX Adibvafa Fallahpour, Jun Ma 0016, Alif Munim, Hongwei Lyu, Bo Wang 0044 |
ICML | 2 |
| 2025 | Exploring the Design Space of 3D MLLMs for CT Report Generation
Mohammed Baharoon, Jun Ma 0016, Congyu Fang, Augustin Toma, Bo Wang 0044 |
MICCAI (6) | 2 |
| 2025 | Corrigendum to "Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge" [Medical Image Analysis, April 2022, Volume 77, 102333]
Matthias Ivantsits, Leonid Goubergrits, Jan-Martin Kuhnigk, Markus Hüllebrand, Jan Brüning, Tabea Kossen, Boris Pfahringer, Jens Schaller, Andreas Spuler, Titus Kühne, Yizhuan Jia, Xuesong Li 0003, Suprosanna Shit, Bjoern Menze, Ziyu Su, Jun Ma 0016, Ziwei Nie, Kartik Jain, Yi Lin 0009, Anja Hennemuth |
Medical Image Anal. | 16 |
| 2023 | Why is the Winner the Best?abstractInternational benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Tizabi, Fabian Isensee, Tim Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David Gage Ellis, Sandy Engelhardt, Melanie Ganz-Benjaminsen, Noha M. Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, A. Emre Kavur, Oldrich Kodym, Michal Kozubek 0001, Jianning Li 0002, Hongwei Li 0004, Jun Ma 0016, Carlos Martín-Isla, Bjoern Menze, J. Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patino, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin M. H. van Wijnen, Martin Wagner 0001, D. Wei, Amine Yamlahi, Moi Hoon Yap, C. Yuan, Maximilian Zenk, A. Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, J. Cho, C. Choi, Qi Dou 0001, Ivan Ezhov, Christoph M. Friedrich, C. Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García-Faura, Maria Grammatikopoulou, S. Hong, Mostafa Jahanifar, I. Jang, Abdolrahim Kadkhodamohammadi, I. Kang, Florian Kofler, S. Kondo, Hugo J. Kuijf, M. Luu, Tomaz Martincic, Pedro Morais, Mohamed A. Naser, Bruno Oliveira 0002, David Owen 0001, S. Pang, Szymon Plotka, Élodie Puybareau, Nasir M. Rajpoot, K. Ryu, Numan Saeed, Adam J. Shephard, Dejan Stepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Hélène Urien, João L. Vilaça, Kareem A. Wahid, Benedikt Wiestler, Marek Wodzinski, F. Xia, J. Xie, Z. Xiong, Sen Yang 0006, Klaus H. Maier-Hein, Paul F. Jaeger, Annette Kopp-Schneider, Lena Maier-Hein |
CVPR | 42 |
| 2023 | Pre-trained Diffusion Models for Plug-and-Play Medical Image Enhancement
Jun Ma 0016, Yuanzhi Zhu 0001, Chenyu You, Bo Wang 0044 |
MICCAI (3) | 1 |
| 2023 | MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images
Lei Li 0020, Fuping Wu, Xinzhe Luo, Carlos Martín-Isla, Shuwei Zhai, Zhen Zhang 0057, Markus J. Ankenbrand, Haochuan Jiang, Linhong Wang, Tewodros Weldebirhan Arega, Elif Altunok, Jun Ma 0016, Xiaoping Yang 0001, Élodie Puybareau, Ilkay Öksüz, Stéphanie Bricq, Weisheng Li 0001, Kumaradevan Punithakumar, Sotirios A. Tsaftaris, Laura Maria Schreiber, Guocai Liu, Yong Xia 0001, Guotai Wang, Sergio Escalera, Xiahai Zhuang |
Medical Image Anal. | 18 |
| 2023 | The Liver Tumor Segmentation Benchmark (LiTS)abstractIn this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094. Patrick Bilic, Patrick Ferdinand Christ, Hongwei Li 0004, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland H. Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Amitai, Refael Vivanti, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Miriam Bellver, Lei Bi 0001, Hao Chen 0011, Grzegorz Chlebus, Erik Dam, Qi Dou 0001, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Grün, Xu Han 0009, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul F. Jaeger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz K. Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Xiaomeng Li 0001, John S. Lowengrub, Jun Ma 0016, Klaus H. Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Xiaojuan Qi 0001, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Daguang Xu, Xiaoping Yang 0001, Simon C. H. Yu, Yading Yuan, Miao Yue, Liping Zhang 0009, Manuel Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Joseph Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze |
Medical Image Anal. | 71 |
| 2022 | Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge
Matthias Ivantsits, Leonid Goubergrits, Jan-Martin Kuhnigk, Markus Hüllebrand, Jan Brüning, Tabea Kossen, Boris Pfahringer, Jens Schaller, Andreas Spuler, Titus Kühne, Yizhuan Jia, Xuesong Li 0003, Suprosanna Shit, Bjoern Menze, Ziyu Su, Jun Ma 0016, Ziwei Nie, Kartik Jain, Yi Lin 0009, Anja Hennemuth |
Medical Image Anal. | 16 |
| 2022 | Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
Alain Lalande, Zhihao Chen 0005, Thibaut Pommier, Thomas Decourselle, Abdul Qayyum 0002, Michel Salomon, Dominique Ginhac, Youssef Skandarani, Arnaud Boucher, Khawla Brahim, Marleen de Bruijne, Robin Camarasa, Teresa Correia, Xue Feng 0001, Kibrom Berihu Girum, Anja Hennemuth, Markus Hüllebrand, Raabid Hussain, Matthias Ivantsits, Jun Ma 0016, Craig H. Meyer, Jixi Shi, Nikolaos V. Tsekos, Marta Varela, Sen Yang 0006, Hannu Zhang, Yichi Zhang 0007, Yuncheng Zhou, Xiahai Zhuang, Raphaël Couturier, Fabrice Mériaudeau |
Medical Image Anal. | 20 |
| 2022 | Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challengeabstractAutomatic segmentation of abdominal organs in CT scans plays an important role in clinical practice. However, most existing benchmarks and datasets only focus on segmentation accuracy, while the model efficiency and its accuracy on the testing cases from different medical centers have not been evaluated. To comprehensively benchmark abdominal organ segmentation methods, we organized the first Fast and Low GPU memory Abdominal oRgan sEgmentation (FLARE) challenge, where the segmentation methods were encouraged to achieve high accuracy on the testing cases from different medical centers, fast inference speed, and low GPU memory consumption, simultaneously. The winning method surpassed the existing state-of-the-art method, achieving a 19× faster inference speed and reducing the GPU memory consumption by 60% with comparable accuracy. We provide a summary of the top methods, make their code and Docker containers publicly available, and give practical suggestions on building accurate and efficient abdominal organ segmentation models. The FLARE challenge remains open for future submissions through a live platform for benchmarking further methodology developments at https://flare.grand-challenge.org/. Jun Ma 0016, Yao Zhang 0010, Song Gu, Xingle An, Zhihe Wang, Cheng Ge, Yinan Xu 0004, Shuiping Gou, Franz Thaler, Christian Payer, Darko Stern, Edward G. A. Henderson, Dónal M. McSweeney, Andrew Green 0001, Price Jackson, Lachlan McIntosh, Quoc-Cuong Nguyen, Abdul Qayyum 0002, Pierre-Henri Conze, Ziyan Huang, Deng-Ping Fan, Huan Xiong, Guoqiang Dong, Qiongjie Zhu, Xiaoping Yang 0001 |
Medical Image Anal. | 1 |
| 2022 | Head and neck tumor segmentation in PET/CT: The HECKTOR challengeabstractThis paper relates the post-analysis of the first edition of the HEad and neCK TumOR (HECKTOR) challenge. This challenge was held as a satellite event of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020, and was the first of its kind focusing on lesion segmentation in combined FDG-PET and CT image modalities. The challenge's task is the automatic segmentation of the Gross Tumor Volume (GTV) of Head and Neck (H&N) oropharyngeal primary tumors in FDG-PET/CT images. To this end, the participants were given a training set of 201 cases from four different centers and their methods were tested on a held-out set of 53 cases from a fifth center. The methods were ranked according to the Dice Score Coefficient (DSC) averaged across all test cases. An additional inter-observer agreement study was organized to assess the difficulty of the task from a human perspective. 64 teams registered to the challenge, among which 10 provided a paper detailing their approach. The best method obtained an average DSC of 0.7591, showing a large improvement over our proposed baseline method and the inter-observer agreement, associated with DSCs of 0.6610 and 0.61, respectively. The automatic methods proved to successfully leverage the wealth of metabolic and structural properties of combined PET and CT modalities, significantly outperforming human inter-observer agreement level, semi-automatic thresholding based on PET images as well as other single modality-based methods. This promising performance is one step forward towards large-scale radiomics studies in H&N cancer, obviating the need for error-prone and time-consuming manual delineation of GTVs. Valentin Oreiller, Vincent Andrearczyk, Mario Jreige, Sarah Boughdad, Hesham Elhalawani, Joël Castelli, Martin Vallières, Simeng Zhu, Juanying Xie, Andrei Iantsen, Mathieu Hatt, Yading Yuan, Jun Ma 0016, Xiaoping Yang 0001, Chinmay Rao, Suraj Pai, Kanchan Ghimire, Xue Feng 0001, Mohamed A. Naser, Clifton D. Fuller, Fereshteh Yousefi Rizi, Arman Rahmim, Huai Chen, Lisheng Wang, John O. Prior, Adrien Depeursinge |
Medical Image Anal. | 14 |
| 2022 | AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?abstractWith the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods. Jun Ma 0016, Yao Zhang 0010, Song Gu, Cheng Ge, Yichi Zhang 0007, Xingle An, Shucheng Cao, Qi Zhang 0059, Shangqing Liu, Xiaoping Yang 0001 |
IEEE Trans. Pattern Anal. Mach. Intell. | 1 |
| 2021 | Loss odyssey in medical image segmentation
Jun Ma 0016, Jianan Chen 0001, Matthew Ng, Yu Li 0031, Xiaoping Yang 0001, Anne L. Martel |
Medical Image Anal. | 1 |
| 2021 | A Characteristic Function-Based Algorithm for Geodesic Active ContoursabstractActive contour models have been widely used in image segmentation, and the level set method (LSM) is the most popular approach for solving the models, via implicitly representing the contour by a level set function. However, the LSM suffers from high computational burden and numerical instability, requiring additional regularization terms or reinitialization techniques. In this paper, we use characteristic functions to implicitly represent the contours, propose a new representation to the geodesic active contours, and derive an efficient algorithm termed the iterative convolution-thresholding method (ICTM). Compared to the LSM, the ICTM is simpler and much more efficient. In addition, the ICTM enjoys most desired features of the level set--based methods. Extensive experiments, on two-dimensional (2D) synthetic, 2D ultrasound, 3D computed tomography, and 3D magnetic resonance images for nodule, organ, and lesion segmentation demonstrate that the proposed method not only obtains comparable or even better segmentation results (compared to the LSM) but also achieves significant acceleration. Jun Ma 0016, Dong Wang 0008, Xiaoping Wang 0002, Xiaoping Yang 0001 |
SIAM J. Imaging Sci. | 1 |
| 2021 | Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms ChallengeabstractThe emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field. Víctor M. Campello, Polyxeni Gkontra, Cristian Izquierdo, Carlos Martín-Isla, Alireza Sojoudi, Peter M. Full, Klaus H. Maier-Hein, Yao Zhang 0010, Zhiqiang He 0002, Jun Ma 0016, Mario Parreño, Alberto Albiol, Fanwei Kong, Shawn C. Shadden, Jorge Corral Acero, Vaanathi Sundaresan, Mina Saber, Mustafa A. Alattar, Hongwei Li 0004, Bjoern Menze, Firas Khader, Christoph Haarburger, Cian M. Scannell, Mitko Veta, Adam Carscadden, Kumaradevan Punithakumar, Xiao Liu 0037, Sotirios A. Tsaftaris, Xiaoqiong Huang, Xin Yang 0009, Lei Li 0020, Xiahai Zhuang, David Viladés, Martín Luís Descalzo, Andrea Guala 0002, Lucia La Mura, Matthias G. W. Friedrich, Ria Garg, Julie Lebel, Filipe Henriques, Mahir Karakas, Ersin Çavus, Steffen E. Petersen, Sergio Escalera, Santi Seguí, Jose Rodriguez-Palomares, Karim Lekadir |
IEEE Trans. Medical Imaging | 10 |
| 2021 | Learning Geodesic Active Contours for Embedding Object Global Information in Segmentation CNNsabstractMost existing CNNs-based segmentation methods rely on local appearances learned on the regular image grid, without consideration of the object global information. This article aims to embed the object global geometric information into a learning framework via the classical geodesic active contours (GAC). We propose a level set function (LSF) regression network, supervised by the segmentation ground truth, LSF ground truth and geodesic active contours, to not only generate the segmentation probabilistic map but also directly minimize the GAC energy functional in an end-to-end manner. With the help of geodesic active contours, the segmentation contour, embedded in the level set function, can be globally driven towards the image boundary to obtain lower energy, and the geodesic constraint can lead the segmentation result to have fewer outliers. Extensive experiments on four public datasets show that (1) compared with state-of-the-art (SOTA) learning active contour methods, our method can achieve significantly better performance; (2) compared with recent SOTA methods that are designed for reducing boundary errors, our method also outperforms them with more accurate boundaries; (3) compared with SOTA methods on two popular multi-class segmentation challenge datasets, our method can still obtain superior or competitive results in both organ and tumor segmentation tasks. Our study demonstrates that introducing global information by GAC can significantly improve segmentation performance, especially on reducing the boundary errors and outliers, which is very useful in applications such as organ transplantation surgical planning and multi-modality image registration where boundary errors can be very harmful. Jun Ma 0016, Xiaoping Yang 0001 |
IEEE Trans. Medical Imaging | 1 |
| 2021 | Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 ChallengeabstractTo better understand early brain development in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; h owever, one of the major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, the iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. T raining/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participated in the iSeg-2019. In this article, 8 top-ranked methods were reviewed by detailing their pipelines/implementations, presenting experimental results, and evaluating performance across different sites in terms of whole brain, regions of interest, and gyral landmark curves. We further pointed out their limitations and possible directions for addressing the multi-site issue. We find that multi-site consistency is still an open issue. We hope that the multi-site dataset in the iSeg-2019 and this review article will attract more researchers to address the challenging and critical multi-site issue in practice. Yue Sun 0001, Kun Gao 0002, Zhengwang Wu, Xiaopeng Zong, Zhihao Lei, Ying Wei 0007, Jun Ma 0016, Xiaoping Yang 0001, Xue Feng 0001, Li Zhao 0001, Trung Le Phan, Jitae Shin, Tao Zhong 0002, Yu Zhang 0064, Lequan Yu, Caizi Li, Ramesh Basnet, M. Omair Ahmad, M. N. S. Swamy 0001, Wenao Ma, Qi Dou 0001, Toan Duc Bui, Camilo Bermudez, Bennett A. Landman, Ian H. Gotlib, Kathryn L. Humphreys, Sarah Shultz, Longchuan Li, Sijie Niu, Weili Lin, Valerie Jewells, Dinggang Shen, Gang Li 0001, Li Wang 0026 |
IEEE Trans. Medical Imaging | 8 |
| 2020 | A Multi-Organ Nucleus Segmentation ChallengeabstractGeneralized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics. Neeraj Kumar 0002, Ruchika Verma, Deepak Anand, Yanning Zhou 0001, Omer Fahri Onder, Efstratios Tsougenis, Hao Chen 0011, Pheng-Ann Heng, Jiahui Li 0005, Navid Alemi Koohbanani, Mostafa Jahanifar, Neda Zamani Tajeddin, Ali Gooya, Nasir M. Rajpoot, Xuhua Ren, Sihang Zhou 0001, Qian Wang 0001, Dinggang Shen, Cheng-Kun Yang, Chi-Hung Weng, Wei-Hsiang Yu, Chao-Yuan Yeh, Shuoyu Xu, Pak-Hei Yeung, Amirreza Mahbod, Gerald Schaefer, Isabella Ellinger, Rupert Ecker, Örjan Smedby, Chunliang Wang, Benjamin Chidester, Vinh Ton-That, Minh-Triet Tran, Jian Ma 0004, Minh N. Do, Simon Graham, Quoc Dang Vu, Jin Tae Kwak, Akshaykumar Gunda, Raviteja Chunduri, Corey Hu, Dariush Lotfi, Reza Safdari, Antanas Kascenas, Alison O'Neil, Dennis Eschweiler, Johannes Stegmaier, Yanping Cui, Kailin Chen, Xinmei Tian 0001, Philipp Grüning, Erhardt Barth, Elad Arbel, Itay Remer, Amir Ben-Dor, Ekaterina Sirazitdinova, Matthias Kohl, Stefan Braunewell, Yuexiang Li, Xinpeng Xie, LinLin Shen, Jun Ma 0016, Krishanu Das Baksi, Mohammad Azam Khan, Jaegul Choo, Adrián Colomer, Valery Naranjo, Linmin Pei, Khan M. Iftekharuddin, Kaushiki Roy, Debotosh Bhattacharjee, Aníbal Pedraza, Gloria Bueno García, Sabarinathan Devanathan, Saravanan Radhakrishnan, Praveen Koduganty, Zihan Wu 0001, Guanyu Cai, Amit Sethi |
IEEE Trans. Medical Imaging | 68 |