Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 553

The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.

A Multi-Organ Nucleus Segmentation Challenge

Open access
Kumar, Neeraj, Verma, Ruchika, Anand, Deepak, Zhou, Yanning, Onder, Omer Fahri, Tsougenis, Efstratios, Chen, Hao, Heng, Pheng-Ann, Li, Jiahui, Hu, Zhiqiang, Wang, Yunzhi, Koohbanani, Navid Alemi, Jahanifar, Mostafa, Tajeddin, Neda Zamani, Gooya, Ali, Rajpoot, Nasir, Ren, Xuhua, Zhou, Sihang, Wang, Qian, Shen, Dinggang, Yang, Cheng-Kun, Weng, Chi-Hung, Yu, Wei-Hsiang, Yeh, Chao-Yuan, Yang, Shuang, Xu, Shuoyu, Yeung, Pak Hei, Sun, Peng, Mahbod, Amirreza, Schaefer, Gerald, Ellinger, Isabella, Ecker, Rupert, Smedby, Orjan, Wang, Chunliang, Chidester, Benjamin, Ton, That-Vinh, Tran, Minh-Triet, Ma, Jian, Do, Minh N., Graham, Simon, Vu, Quoc Dang, Kwak, Jin Tae, Gunda, Akshaykumar, Chunduri, Raviteja, Hu, Corey, Zhou, Xiaoyang, Lotfi, Dariush, Safdari, Reza, Kascenas, Antanas, O'Neil, Alison, Eschweiler, Dennis, Stegmaier, Johannes, Cui, Yanping, Yin, Baocai, Chen, Kailin, Tian, Xinmei, Gruening, Philipp, Barth, Erhardt, Arbel, Elad, Remer, Itay, Ben-Dor, Amir, Sirazitdinova, Ekaterina, Kohl, Matthias, Braunewell, Stefan, Li, Yuexiang, Xie, Xinpeng, Shen, Linlin, Ma, Jun, Baksi, Krishanu Das, Khan, Mohammad Azam, Choo, Jaegul, Colomer, Adrian, Naranjo, Valery, Pei, Linmin, Iftekharuddin, Khan M., Roy, Kaushiki, Bhattacharjee, Debotosh, Pedraza, Anibal, Bueno, Maria Gloria, Devanathan, Sabarinathan, Radhakrishnan, Saravanan, Koduganty, Praveen, Wu, Zihan, Cai, Guanyu, Liu, Xiaojie, Wang, Yuqin, Sethi, Amit·2020·IEEE Transactions on Medical Imaging
DOI 10.1109/tmi.2019.2947628
Open this paper in CoChat — free account

Abstract

Generalized 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.

Overview

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tmi.2019.2947628

Cites

50 of 50 references verified0 citation errors · 0 not checkable

Statistics

Verification

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.

A few free checks a day · papers already verified open instantly · results are public

Self-check

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

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

Citations

Cites

Show all 50 references

Cited by

See all 553

Metadata

Authors
Kumar, N., Verma, R., Anand, D., Zhou, Y., Onder, O. F., Tsougenis, E., Chen, H., Heng, P.-A., Li, J., Hu, Z., Wang, Y., Koohbanani, N. A., Jahanifar, M., Tajeddin, N. Z., Gooya, A., Rajpoot, N., Ren, X., Zhou, S., Wang, Q., Shen, D., Yang, C.-K., Weng, C.-H., Yu, W.-H., Yeh, C.-Y., Yang, S., Xu, S., Yeung, P. H., Sun, P., Mahbod, A., Schaefer, G., Ellinger, I., Ecker, R., Smedby, O., Wang, C., Chidester, B., Ton, T.-V., Tran, M.-T., Ma, J., Do, M. N., Graham, S., Vu, Q. D., Kwak, J. T., Gunda, A., Chunduri, R., Hu, C., Zhou, X., Lotfi, D., Safdari, R., Kascenas, A., O'Neil, A., Eschweiler, D., Stegmaier, J., Cui, Y., Yin, B., Chen, K., Tian, X., Gruening, P., Barth, E., Arbel, E., Remer, I., Ben-Dor, A., Sirazitdinova, E., Kohl, M., Braunewell, S., Li, Y., Xie, X., Shen, L., Ma, J., Baksi, K. D., Khan, M. A., Choo, J., Colomer, A., Naranjo, V., Pei, L., Iftekharuddin, K. M., Roy, K., Bhattacharjee, D., Pedraza, A., Bueno, M. G., Devanathan, S., Radhakrishnan, S., Koduganty, P., Wu, Z., Cai, G., Liu, X., Wang, Y., & Sethi, A.
Venue
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020-05
Volume
39
Issue
5
Pages
1380-1391
ISSN
  • 0278-0062 (ISSN-L)
  • 1558-254X
DOI
10.1109/tmi.2019.2947628
OpenAlex
W2981994674
Cited by
553
References
50
FWCI
17.73
Type
journal article
Language
en
Open access
green

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A Multi-Organ Nucleus Segmentation Challenge. https://app.cochat.ai/papers/10.1109/tmi.2019.2947628
The paper
Kumar, N., Verma, R., Anand, D., Zhou, Y., Onder, O. F., Tsougenis, E., Chen, H., Heng, P.-A., Li, J., Hu, Z., Wang, Y., Koohbanani, N. A., Jahanifar, M., Tajeddin, N. Z., Gooya, A., Rajpoot, N., Ren, X., Zhou, S., Wang, Q., . . . Sethi, A. (2020). A Multi-Organ Nucleus Segmentation Challenge. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2019.2947628

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.