Cited by 857Medical Image Segmentation TechniquesBrain Tumor Detection and ClassificationMedical Imaging and Analysis

The DOI matches its Crossref record; 53 of the 56 references it cites were found in a registry or an index.

Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge

Open access

Litjens, Geert, Toth, Robert, van de Ven, Wendy, Hoeks, Caroline, Kerkstra, Sjoerd, van Ginneken, Bram, Vincent, Graham, Guillard, Gwenael, Birbeck, Neil, Zhang, Jindang, Strand, Robin, Malmberg, Filip, Ou, Yangming, Davatzikos, Christos, Kirschner, Matthias, Jung, Florian, Yuan, Jing, Qiu, Wu, Gao, Qinquan, Edwards, Philip “Eddie”, Maan, Bianca, van der Heijden, Ferdinand, Ghose, Soumya, Mitra, Jhimli, Dowling, Jason, Barratt, Dean, Huisman, Henkjan, Madabhushi, Anant·2014·Medical Image Analysis

DOI 10.1016/j.media.2013.12.002PMC PMC4137968

Open this paper in CoChat — free account Open PDF

Abstract

Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p<0.05) and had an efficient implementation with a run time of 8min and 3s per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.media.2013.12.002

Cites

53 of 53 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
Marching cubes: A high resolution 3D surface construction algorithm
Lorensen, W. E., & Cline, H. E. (1987) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/37401.37422 · Cited by 10,320
Marching cubes: A high resolution 3D surface construction algorithm
Lorensen, W. E., & Cline, H. E. (1987) · ACM SIGGRAPH Computer Graphics · doi:10.1145/37402.37422 · Cited by 8,928
Active appearance models
Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927467 · Cited by 5,409
ESUR prostate MR guidelines 2012
Barentsz, J. O., Richenberg, J., Clements, R., Choyke, P. L., Verma, S., Villeirs, G. M., Rouvière, O., Løgager, V., & Fütterer, J. J. (2012) · European Radiology · doi:10.1007/s00330-011-2377-y · Cited by 2,444
Robust real-time object detection.
Viola, P. A. (2001) · Cited by 2,340
Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation
Warfield, S. K., Zou, K. H., & Wells, W. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.828354 · Cited by 2,072
Poisson image editing
Pérez, P., Gangnet, M., & Blake, A. (2003) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1201775.882269 · Cited by 2,041
Poisson image editing
Pérez, P., Gangnet, M., & Blake, A. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882269 · Cited by 1,769
Comparison and evaluation of methods for liver segmentation from CT datasets
Heimann, T., van Ginneken, B., Styner, M. A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., et al. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2013851 · Cited by 1,135

Show all 56 references

Research works printed but not linked: 8

Birkbeck, N., Zhang, J., Requardt, M., Kiefer, B., Gall, P., Kevin Zhou, S., 2012. Region-specific hierarchical segmentation of MR prostate using discriminative learning. MICCAI Grand Challenge: Prostate MR Image Segmentation 2012.
Gao, Q., Rueckert, D., Edwards, P., 2012. An automatic multi-atlas based prostate segmentation using local appearance-specific atlases and patch-based voxel weighting. MICCAI Grand Challenge: Prostate MR Image Segmentation 2012.
Ghose, S., Mitra, J., Oliver, A., Martí, R., Lladó, X., Freixenet, J., Vilanova, J.C., Sidibé, D., Meriaudeau, F., 2012. A random forest based classification approach to prostate segmentation in MRI. MICCAI Grand Challenge: Prostate MR Image Segmentation 2012.
Kirschner, M., Jung, F., Wesarg, S., 2012. Automatic prostate segmentation in MR images with a probabilistic active shape model. MICCAI Grand Challenge: Prostate MR Image Segmentation 2012.
Litjens, G.J.S., Karssemeijer, N., Huisman, H.J., 2012. A multi-atlas approach for prostate segmentation in MR images. MICCAI Grand Challenge: Prostate MR Image Segmentation 2012.
Toth, 2012, Multifeature landmark-free active appearance models: application to prostate MRI segmentation, IEEE Trans. Med. Imaging, 31, 1638, 10.1109/TMI.2012.2201498 · DOI
Vincent, G., Guillard, G., Bowes, M., 2012. Fully automatic segmentation of the prostate using active appearance models. MICCAI Grand Challenge: Prostate MR Image Segmentation 2012.
Yuan, J., Qiu, W., Ukwatta, E., Rajchl, M., Sun, Y., Fenster, A., 2012. An efficient convex optimization approach to 3D prostate MRI segmentation with generic star shape prior. MICCAI Grand Challenge: Prostate MR Image Segmentation 2012.

Sorted by citations

Items 301–400 of 856

Development and clinical implementation of SeedNet: A sliding‐window convolutional neural network for radioactive seed identification in MRI‐assisted radiosurgery (MARS)
Sanders, J. W., Frank, S. J., Kudchadker, R. J., Bruno, T. L., & Ma, J. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27677 · Cited by 20
Deep Learning Techniques for Prostate Cancer Analysis and Detection: Survey of the State of the Art
Olawuyi, O., & Viriri, S. (2025) · Journal of Imaging · doi:10.3390/jimaging11080254 · Cited by 19
Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images
Salvi, M., Santi, B. D., Pop, B., Bosco, M., Giannini, V., Regge, D., Molinari, F., & Meiburger, K. M. (2022) · Journal of Imaging · doi:10.3390/jimaging8050133 · Cited by 19
Automatic prostate and peri-prostatic fat segmentation based on pyramid mechanism fusion network for T2-weighted MRI
Li, Y., Wu, Y., Huang, M., Zhang, Y., & Bai, Z. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106918 · Cited by 19
Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI
Thimansson, E., Bengtsson, J., Baubeta, E., Engman, J., Flondell-Sité, D., Bjartell, A. S., & Zackrisson, S. (2022) · European Radiology · doi:10.1007/s00330-022-09239-8 · Cited by 19
Boosting knowledge diversity, accuracy, and stability via tri-enhanced distillation for domain continual medical image segmentation
Zhu, Z., Ma, X., Wang, W., Dong, S., Wang, K., Wu, L., Luo, G., Wang, G., & Li, S. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103112 · Cited by 20
ACFL: Communication-Efficient adversarial contrastive federated learning for medical image segmentation
Liang, Z., Zhao, K., Liang, G., Wu, Y., & Guo, J. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112516 · Cited by 19
Mixup (Sample Pairing) Can Improve the Performance of Deep Segmentation Networks
Isaksson, L. J., Summers, P. E., Raimondi, S., Gandini, S., Bhalerao, A. H., Marvaso, G., Petralia, G., Pepa, M., & Jereczek‐Fossa, B. A. (2021) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2022-0003 · Cited by 19
Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation
Rouvière, O., Moldovan, P. C., Vlachomitrou, A. S., Gouttard, S., Riche, B., Groth, A., Rabotnikov, M., Ruffion, A., Colombel, M. C., Crouzet, S., et al. (2022) · European Radiology · doi:10.1007/s00330-021-08408-5 · Cited by 19
Comparative Local Quality Assessment of 3D Medical Image Segmentations with Focus on Statistical Shape Model-Based Algorithms
von Landesberger, T., Basgier, D., & Becker, M. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2501813 · Cited by 21
Automatic prostate segmentation on MR images with deep network and graph model
Yan, K., Li, C., Wang, X., Li, A., Yuan, Y., Feng, D. D., Khadra, M. H., & Kim, J. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590782 · Cited by 19
Manual prostate MRI segmentation by readers with different experience: a study of the learning progress
Langkilde, F., Masaba, P., Edenbrandt, L., Gren, M., Halil, A., Hellström, M., Larsson, M., Naeem, A. A., Wallström, J., Maier, S. E., et al. (2024) · European Radiology · doi:10.1007/s00330-023-10515-4 · Cited by 18
Adaptive weighted multi-teacher distillation for efficient medical imaging segmentation with limited data
Loussaief, E. B., Rashwan, H. A., Ayad, M., Khalid, A., & Puig, D. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113196 · Cited by 18
Active Contour Model in Deep Learning Era: A Revise and Review
Le, N., Luu, K., Duong, C. N., Quach, K. G., Truong, T.-D., Sadler, K., & Savvides, M. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-40977-7_11 · Cited by 20
Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI
Astono, I. P., Welsh, J. S., Chalup, S. K., & Greer, P. B. (2020) · Applied Sciences · doi:10.3390/app10072601 · Cited by 18
3D multi-scale discriminative network with multi-directional edge loss for prostate zonal segmentation in bi-parametric MR images
Qin, X., Zhu, Y. M., Wang, W., Gui, S., Zheng, B., & Wang, P. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.116 · Cited by 18
Comprehensive Importance-Based Selective Regularization for Continual Segmentation Across Multiple Sites
Zhang, J., Gu, R., Wang, G., & Gu, L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87193-2_37 · Cited by 18
Self-citation
DFTNet: Dual-Path Feature Transfer Network for Weakly Supervised Medical Image Segmentation
Cai, W., Xie, L., Yang, W., Li, Y., Gao, Y., & Wang, T. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3198284 · Cited by 18
Densely Dilated Spatial Pooling Convolutional Network Using Benign Loss Functions for Imbalanced Volumetric Prostate Segmentation
Liu, Q., Fu, M., Jiang, H., & Gong, X. (2020) · Current Bioinformatics · doi:10.2174/1574893615666200127124145 · Cited by 18
Semi-Supervised Medical Image Segmentation Based on Deep Consistent Collaborative Learning
Zhao, X., & Wang, W. (2024) · Journal of Imaging · doi:10.3390/jimaging10050118 · Cited by 17
β-FFT: Nonlinear Interpolation and Differentiated Training Strategies for Semi-Supervised Medical Image Segmentation
Hu, M., Yin, J., Ma, Z., Ma, J., Zhu, F., Wu, B., Wen, Y., Wu, M., Hu, C., Hu, B., et al. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02872 · Cited by 17
Deep Learning for Image Segmentation: A Focus on Medical Imaging
Khalifa, A. F., & Badr, E. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.035888 · Cited by 17
Blurred trace infrared image segmentation based on template approach and immune factor
Yu, X. (2014) · Infrared Physics & Technology · doi:10.1016/j.infrared.2014.07.002 · Cited by 17
Automatic Prostate Cancer Detection On Multi-Parametric Mri With Hierarchical Weakly Supervised Learning
Yang, H., Wu, G., Shen, D. G., & Liao, S. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434108 · Cited by 17
Comparison of automated segmentation techniques for magnetic resonance images of the prostate
Isaksson, L. J., Pepa, M., Summers, P. E., Zaffaroni, M., Vincini, M. G., Corrao, G., Mazzola, G. C., Rotondi, M., Presti, G. L., Raimondi, S., et al. (2023) · BMC Medical Imaging · doi:10.1186/s12880-023-00974-y · Cited by 17
Improving Prostate Whole Gland Segmentation In T2-Weighted MRI With Synthetically Generated Data
Fernández-Quilez, Á., Larsen, S. V., Goodwin, M., Gulsrud, T. O., Kjosavik, S. R., & Oppedal, K. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433793 · Cited by 16
Shape-Aware Joint Distribution Alignment for Cross-Domain Image Segmentation
Wang, P., Peng, J., Pedersoli, M., Zhou, Y., Zhang, C., & Desrosiers, C. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3247941 · Cited by 16
Consistency-Guided Meta-learning for Bootstrapping Semi-supervised Medical Image Segmentation
Wei, Q., Yu, L., Li, X., Shao, W., Xie, C., Xing, L., & Zhou, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43901-8_18 · Cited by 16
V-Net Light - Parameter-Efficient 3-D Convolutional Neural Network for Prostate MRI Segmentation
Yaniv, O., Portnoy, O., Talmon, A., Kiryati, N., Konen, E., & Mayer, A. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098643 · Cited by 16
Automatic brain extraction from 3D fetal MR image with deep learning-based multi-step framework
Chen, J., Fang, Z., Zhang, G., Lei, L., Li, G., Zhang, H., & Wang, L. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101848 · Cited by 16
Estimating Model Performance Under Domain Shifts with Class-Specific Confidence Scores
Li, Z., Kamnitsas, K., Islam, M., Chen, C., & Glocker, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16449-1_66 · Cited by 16
A Multi-Scale Channel Attention Network for Prostate Segmentation
Ding, M., Lin, Z., Lee, C. H., Tan, C. H., & Huang, W. (2023) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2023.3257728 · Cited by 16
REliability of consensus-based segMentatIoN in raDiomic feature reproducibility (REMIND): A word of caution
Koçak, B., Yardımcı, A. H., Nazlı, M. A., Yüzkan, S., Mutlu, S., Güzelbey, T., Özdemir, M. Ş., Akin, M., Yücel, S., Bulut, E., et al. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110893 · Cited by 16
Spatially varying accuracy and reproducibility of prostate segmentation in magnetic resonance images using manual and semiautomated methods
Shahedi, M., Cool, D. W., Romagnoli, C., Bauman, G. S., Bastian‐Jordan, M., Gibson, E., Rodrigues, G., Ahmad, B., Lock, M. I., Fenster, A., et al. (2014) · Medical Physics · doi:10.1118/1.4899182 · Cited by 18
A Transfer Learning Approach for Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion Weighted MRIPreprint
Motamed, S., Gujrathi, I., Deniffel, D., Oentoro, A., Haider, M. A., & Khalvati, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.09541 · Cited by 18
Deep learning in magnetic resonance prostate segmentation: A review and a new perspectivePreprint
Gillespie, D., Kendrick, C., Boon, I. S., Boon, C. S., Rattay, T., & Yap, M. H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.07795 · Cited by 16
Variational methods with application to medical image segmentation: A survey
Shu, X., Li, Z., Chang, X., & Yuan, D. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130260 · Cited by 15
MedFusion-TransNet: multi-modal fusion via transformer for enhanced medical image segmentation
Sun, J. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1557449 · Cited by 15
Computer-aided segmentation on MRI for prostate radiotherapy, Part I: Quantifying human interobserver variability of the prostate and organs at risk and its impact on radiation dosimetry
Sanders, J. W., Mok, H., Hanania, A. N., Venkatesan, A. M., Tang, C. Z., Bruno, T. L., Thames, H. D., Kudchadker, R. J., & Frank, S. J. (2021) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2021.12.011 · Cited by 18
Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data
Rodrigues, N. M., de Almeida, J. G., Verde, A. S. C., Gaivão, A. M., Bilreiro, C., Santiago, I., Ip, J., Belião, S., Moreno, R., Matos, C., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108216 · Cited by 15
Hi- g MISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution p GAN
Showrav, T. T., & Hasan, M. K. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad3cb3 · Cited by 15
DB-SAM: Delving into High Quality Universal Medical Image Segmentation
Qin, C., Cao, J., Fu, H., Khan, F. S., & Anwer, R. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72390-2_47 · Cited by 15
Enhancing semi-supervised medical image segmentation with boundary awareness and Competitive Contrastive Learning
Lin, Z., Li, Z., Su, J., Li, L., & Lian, S. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107450 · Cited by 15
Multi-view cross-consistency and multi-scale cross-layer contrastive learning for semi-supervised medical image segmentation
Cao, X., Guo, X., Yang, G., Chen, Q., & Dong, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127223 · Cited by 15
Multidisciplinary Consensus Prostate Contours on Magnetic Resonance Imaging: Educational Atlas and Reference Standard for Artificial Intelligence Benchmarking
Song, Y., Dornisch, A. M., Dess, R. T., Margolis, D., Weinberg, E., Barrett, T., Cornell, M., Fan, R. E., Harisinghani, M. G., Kamran, S. C., et al. (2025) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2025.03.024 · Cited by 15
Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction
Virgolin, M., Wang, Z., Alderliesten, T., & Bosman, P. A. N. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.4.046501 · Cited by 17
NMNet: Learning Multi-level semantic information from scale extension domain for improved medical image segmentation
Song, E., Zhan, B., Liu, H., Cetinkaya, C., & Hung, C. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104651 · Cited by 15
Rotationally resliced 3D prostate TRUS segmentation using convex optimization with shape priors
Qiu, W., Yuan, J., Ukwatta, E., & Fenster, A. (2015) · Medical Physics · doi:10.1118/1.4906129 · Cited by 15
Self-citation
Automatic segmentation of prostate zonesPreprint
Mooij, G., Bagulho, I. C., & Huisman, H. J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.07146 · Cited by 15
Self-citation
Hcdg: A Hierarchical Consistency Framework for Domain Generalization on Medical Image SegmentationPreprint
Yang, Y., Wang, S., Zhu, L., & Yu, L. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4188684 · Cited by 19
RaNet: a residual attention network for accurate prostate segmentation in T2-weighted MRI
Arshad, M., Wang, C., Sima, M. W. U., Shaikh, J. A., Alkhalaf, S., & Alturise, F. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1589707 · Cited by 14
FEW-SHOT Image Segmentation for Cross-Institution Male Pelvic Organs Using Registration-Assisted Prototypical Learning
Li, Y., Fu, Y., Yang, Q., Min, Z., Wen, Y., Huisman, H. J., Barratt, D. C., Prisacariu, V. A., & Hu, Y. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761453 · Cited by 14
Self-citation
DMC-UNet-Based Segmentation of Lung Nodules
Fan, X., Lu, Y., Hou, J., Lin, F., Huang, Q., & Yan, C. (2023) · IEEE Access · doi:10.1109/access.2023.3322437 · Cited by 14
Weakly supervised image segmentation beyond tight bounding box annotations
Wang, J., & Xia, B. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107913 · Cited by 15
Online Robust Projective Dictionary Learning: Shape Modeling for MR-TRUS Registration
Wang, Y., Zheng, Q., & Heng, P. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2777870 · Cited by 14
Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy
Girum, K. B., Créhange, G., Hussain, R., WALKER, P. M., & Lalande, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32486-5_15 · Cited by 14
Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation
Birbiri, U. C., Hamidinekoo, A., Amelie, G., Malcolm, P., & Reyer Zwiggelaar, A. (2020) · Journal of Imaging · doi:10.3390/jimaging6090083 · Cited by 14
An Improved U-Net for Human Sperm Head Segmentation
Lv, Q., Yuan, X., Qian, J., Li, X., Zhang, H., & Zhan, S. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10643-2 · Cited by 14
Automatic segmentation of prostate MRI based on 3D pyramid pooling Unet
Li, Y., Lin, C., Zhang, Y., Feng, S., Huang, M., & Bai, Z. (2022) · Medical Physics · doi:10.1002/mp.15895 · Cited by 15
Domain generalization of deep networks for medical image segmentation via meta learning
Liu, Q., Dou, Q., Chen, C., & Heng, P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-399851-2.00016-8 · Cited by 14
Prostate Segmentation From 3D Mri Using A Two-Stage Model and Variable-Input Based Uncertainty Measure
Pan, H., Feng, B. Y., Chen, Q., Meyer, C. H., & Feng, X. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759300 · Cited by 14
Isolated Pulmonary Nodules Characteristics Detection Based on CT Images
Qiu, S., Guo, Q., Zhou, D., Jin, Y., Zhou, T., & He, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2951762 · Cited by 14
Domain-Aware Dual Attention for Generalized Medical Image Segmentation on Unseen Domains
Lai, H., Luo, Y., Li, B., Zhang, G., & Lu, J. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3251380 · Cited by 13
Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning
Rasoulian, A., Salari, S., & Xiao, Y. (2023) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2023-553a · Cited by 13
Instructive Feature Enhancement for Dichotomous Medical Image Segmentation
Liu, L., Zhou, H., Chen, J., Liu, S., Shi, W., Ni, D., Fan, D.-P., & Yang, X. S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43901-8_42 · Cited by 13
Memory Replay for Continual Medical Image Segmentation Through Atypical Sample Selection
Bera, S., Ummadi, V., Sen, D., Mandal, S., & Biswas, P. K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43901-8_49 · Cited by 13
Robustness of magnetic resonance imaging and positron emission tomography radiomic features in prostate cancer: Impact on recurrence prediction after radiation therapy
Dutta, A., Chan, J. C. Y., Haworth, A., Dubowitz, D. J., Kneebone, A. B., & Reynolds, H. M. (2023) · Physics and Imaging in Radiation Oncology · doi:10.1016/j.phro.2023.100530 · Cited by 13
A mixed Mamba U-net for prostate segmentation in MR images
Du, Q., Wang, L., & Chen, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-71045-7 · Cited by 15
Longitudinal Analysis of Pre-Term Neonatal Cerebral Ventricles From 3D Ultrasound Images Using Spatial-Temporal Deformable Registration
Qiu, W., Chen, Y., Kishimoto, J., de Ribaupierre, S., Chiu, B. C. Y., Fenster, A., Menon, B. K., & Yuan, J. Y. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2643635 · Cited by 13
Self-citation
Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine
Bojorquez, J. Z., Jodoin, P., Bricq, S., WALKER, P. M., Brunotte, F., & Lalande, A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0211944 · Cited by 13
Voxelwise Prediction of Recurrent High-Grade Glioma via Proximity Estimation–Coupled Multidimensional Support Vector Machine
Lao, Y., Ruan, D., Vassantachart, A., Fan, Z., Ye, J. C., Chang, E. L., Chin, R., Kaprealian, T. B., Zada, G., Shiroishi, M. S., et al. (2021) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2021.12.153 · Cited by 13
S 3 R: Shape and Semantics-Based Selective Regularization for Explainable Continual Segmentation Across Multiple Sites
Zhang, J., Gu, R., Xue, P., Liu, M., Zheng, H., Zheng, Y., Ma, L., Wang, G., & Gu, L. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3260974 · Cited by 13
Self-citation
The Novel Green Learning Artificial Intelligence for Prostate Cancer Imaging
Kaneko, M., Magoulianitis, V., Ramacciotti, L. S., Raman, A., Paralkar, D., Chen, A., Chu, T. N., Yang, Y., Xue, J., Yang, J., et al. (2023) · Urologic Clinics of North America · doi:10.1016/j.ucl.2023.08.001 · Cited by 13
Visual saliency-based active learning for prostate magnetic resonance imaging segmentation
Mahapatra, D., & Buhmann, J. M. (2016) · Journal of medical imaging · doi:10.1117/1.jmi.3.1.014003 · Cited by 13
Tele-Operated MRI-Guided Needle Insertion for Prostate Interventions
Moreira, P. L. D. F., Kuil, L., Dias, P. I. R. C., Borra, R., & Misra, S. (2018) · Journal of Medical Robotics Research · doi:10.1142/s2424905x18420035 · Cited by 13
CDA-Net for Automatic Prostate Segmentation in MR Images
Lu, Z., Zhao, M., & Pang, Y. (2020) · Applied Sciences · doi:10.3390/app10196678 · Cited by 13
MRI pulse sequence integration for deep‐learning‐based brain metastases segmentation
Yi, D., Grøvik, E., Tong, E., Iv, M., Emblem, K. E., Nilsen, L. B., Saxhaug, C., Latysheva, A., Jacobsen, K. D., Helland, Å., et al. (2021) · Medical Physics · doi:10.1002/mp.15136 · Cited by 13
Fully automated detection of prostate transition zone tumors on T2‐weighted and apparent diffusion coefficient (ADC) map MR images using U‐Net ensemble
Wong, T., Schieda, N., Sathiadoss, P., Haroon, M. A., Abreu‐Gomez, J., & Ukwatta, E. (2021) · Medical Physics · doi:10.1002/mp.15181 · Cited by 13
Dataset of prostate MRI annotated for anatomical zones and cancer
Adams, L. C., Makowski, M. R., Engel, G., Rattunde, M., Busch, F., Asbach, P., Niehues, S. M., Vinayahalingam, S., van Ginneken, B., Litjens, G. J. S., et al. (2022) · Data in Brief · doi:10.1016/j.dib.2022.108739 · Cited by 13
Self-citation
Training Convolutional Networks for Prostate Segmentation With Limited Data
Saunders, S. L., Leng, E., Spilseth, B., Wasserman, N. F., Metzger, G. J., & Bolan, P. J. (2021) · IEEE Access · doi:10.1109/access.2021.3100585 · Cited by 12
How Artificial Intelligence is Transforming Medicine: The Future of Pharmaceutical Research
Sharma, P., Jain, V., & Tailang, M. (2023) · Advanced technologies and societal change · doi:10.1007/978-981-99-1949-9_7 · Cited by 12
An Uncertainty-Guided Tiered Self-training Framework for Active Source-Free Domain Adaptation in Prostate Segmentation
Luo, Z., Luo, X., Gao, Z.-J., & Wang, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72114-4_11 · Cited by 12
FedEvi: Improving Federated Medical Image Segmentation via Evidential Weight Aggregation
Chen, J., Ma, B., Cui, H., & Xia, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72117-5_34 · Cited by 12
CriDiff: Criss-Cross Injection Diffusion Framework via Generative Pre-train for Prostate Segmentation
Liu, T., Zhang, M., Liu, L., Zhong, J., Wang, S., Piao, Y., & Lu, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72111-3_10 · Cited by 12
Multimodality imaging in prostate cancer diagnosis using artificial intelligence: basic concepts and current state-of-the-art
Ayyad, S. M., Abdel-Hamid, N. B., Ali, H. A., & Labib, L. M. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20786-2 · Cited by 12
Automatic MRI prostate segmentation using 3D deeply supervised FCN with concatenated atrous convolution
Wang, B., Lei, Y., Jeong, J. J., Wang, T., Liu, Y., Tian, S., Patel, P. R., Jiang, X., Jani, A. B., Mao, H. Z., et al. (2019) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2512551 · Cited by 12
Active learning strategy and hybrid training for infarct segmentation on diffusion MRI with a U-shaped network
Olivier, A., Moal, O., Moal, B., Munsch, F., Okubo, G., Sibon, I., Dousset, V., & Tourdias, T. (2019) · Journal of medical imaging · doi:10.1117/1.jmi.6.4.044001 · Cited by 12
Applications of artificial intelligence in prostate cancer imaging
Baltzer, P., & Clauser, P. (2021) · Current Opinion in Urology · doi:10.1097/mou.0000000000000887 · Cited by 12
A supervoxel-based segmentation method for prostate MR images
Tian, Z., Liu, L., & Fei, B. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2082255 · Cited by 12
Fast automated segmentation of multiple objects via spatially weighted shape learning
Chandra, S. S., Dowling, J., Greer, P. B., Martin, J. M., WRATTEN, C. R., Pichler, P., Fripp, J., & Crozier‬, ‪. (2016) · Physics in Medicine and Biology · doi:10.1088/0031-9155/61/22/8070 · Cited by 12
Self-citation
Towards a universal MRI atlas of the prostate and prostate zones
Padgett, K. R., Swallen, A., Pirozzi, S., Piper, J., Chinea, F. M., Abramowitz, M. C., Nelson, A. S., Pollack, A. S., & Stoyanova, R. S. (2018) · Strahlentherapie und Onkologie · doi:10.1007/s00066-018-1348-5 · Cited by 14
Diffusion MRI microstructure models with in vivo human brain Connectom data: results from a multi-group comparisonPreprint
Ferizi, U., Scherrer, B., Schneider, T., Alipoor, M., Eufracio, O., Fick, R., Rachid Deriche, B., Nilsson, M., Loya-Olivas, A. K., Rivera, M., et al. (2016) · arXiv (Cornell University) · Cited by 12
MRI Diffusion‐Weighted Imaging to Measure Infarct Volume: Assessment of Manual Segmentation Variability
Cimflová, P., Kral, J., Volný, O., Horn, M., Ojha, P., Čábal, M., Kašičková, L., Havelka, J., Jonszta, T., Bar, M., et al. (2021) · Journal of Neuroimaging · doi:10.1111/jon.12850 · Cited by 12
Self-citation
Deconfounded multi-organ weakly-supervised semantic segmentation via causal intervention
Chen, K., Sun, S., & Du, Y. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102355 · Cited by 11
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation
Hu, J., Yang, Y., Guo, X., Ma, T., & Wang, J. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3406906 · Cited by 13
Dual consistency semi-supervised learning for 3D medical image segmentation
Wei, L., Sha, R., Shi, Y., Wang, Q., Shi, L., & Gao, Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107568 · Cited by 11
KAC-Unet: A Medical Image Segmentation With the Adaptive Group Strategy and Kolmogorov-Arnold Network
Lin, S., Hu, R., Li, Z., Lin, Q., Zeng, K., & Wu, X. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3545699 · Cited by 11
Classification before Segmentation: Improved U-Net Prostate Segmentation
Nowling, R. J., Bukowy, J. D., McGarry, S. D., Nencka, A. S., Blasko, O., Urbain, J., Lowman, A. K., Barrington, A., Banerjee, A., Iczkowski, K. A., et al. (2019) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2019.8834494 · Cited by 11
Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images
Bugeja, J. M., Xia, Y., Chandra, S. S., Murphy, N. J., Eyles, J. P., Spiers, L., Crozier‬, ‪., Hunter, D. J., Fripp, J., & ENGSTROM, C. M. (2022) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-22-332 · Cited by 11
Training Auxiliary Prototypical Classifiers for Explainable Anomaly Detection in Medical Image Segmentation
Cho, W., Park, J.-H., & Choo, J. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00265 · Cited by 11

Authors: Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., Strand, R., Malmberg, F., Ou, Y., Davatzikos, C., Kirschner, M., Jung, F., Yuan, J., Qiu, W., Gao, Q., Edwards, P. “., Maan, B., van der Heijden, F., Ghose, S., Mitra, J., Dowling, J., Barratt, D., Huisman, H., & Madabhushi, A.
Affiliations: Radboud University Medical Center (NL)
Siemens (United States) (US)
Uppsala University (SE)
University of Pennsylvania (US)
Technische Universität Darmstadt (DE)
Robarts Research Institute (CA)
Imperial College London (GB)
University of Twente (NL)
Commonwealth Scientific and Industrial Research Organisation (AU)
University College London (GB)
Case Western Reserve University (US)
Corresponding author: Litjens, Geert
ORCID: Litjens, Geert
van Ginneken, Bram
Vincent, Graham
Guillard, Gwenael
Strand, Robin
Malmberg, Filip
Ou, Yangming
Davatzikos, Christos
Kirschner, Matthias
Yuan, Jing
Qiu, Wu
Gao, Qinquan
Edwards, Philip “Eddie”
van der Heijden, Ferdinand
Ghose, Soumya
Dowling, Jason
Barratt, Dean
Huisman, Henkjan
Madabhushi, Anant
Venue: Medical Image Analysis
Publisher: Elsevier BV
Published: 2014-02
Volume: 18
Issue: 2
Pages: 359-373
ISSN: 1361-8415 (ISSN-L)
DOI: 10.1016/j.media.2013.12.002
PMC: PMC4137968
OpenAlex: W2106033751
Cited by: 857
References: 56
Citations by year: 2026: 107 · 2025: 127 · 2024: 95 · 2023: 106 · 2022: 92 · 2021: 87 · 2020: 71 · 2019: 60 · 2018: 35 · 2017: 25 · 2016: 26 · 2015: 16 · 2014: 10
FWCI: 19.49
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Medical Image Analysis (publishedVersion)
PubMed (publishedVersion)
University of Twente Research Information (publishedVersion)
PubMed Central (acceptedVersion)
UCL Discovery (University College London) (submittedVersion)
Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
Radboud Repository (Radboud University) (submittedVersion)
Radboud Repository (Radboud University) (submittedVersion, other-oa)
University of Twente Research Information (submittedVersion)
Funders: Dutch Cancer Society (KUN2007-3971)
National Cancer Institute of the National Institutes of Health
National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health
National Science Foundation
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
Keywords: prostate segmentation, prostate MRI, active appearance models, multi-atlas registration, level sets, segmentation algorithm comparison, prostate cancer, algorithm combination
MeSH terms: Algorithms, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Prostatic Neoplasms, Reference Standards, Sensitivity and Specificity, Reproducibility of Results, Artifacts, Imaging, Three-Dimensional

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks