# Nuclei Segmentation on Data Science Bowl 2018 (challenge)

Source: [Nuclei Segmentation on Data Science Bowl 2018 (challenge)](<https://www.sota2.com/research/sota/nuclei-segmentation-on-data-science-bowl-2018-challenge>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-26T05:40:22.544Z

Evaluation of Nuclei Segmentation performance on the Data Science Bowl 2018 challenge dataset, reporting metrics such as DSC, mIoU, Recall, and Precision.

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

| Scope | Recorded value |
| --- | --- |
| Task | Nuclei segmentation |
| Dataset (including any stated split) | Data Science Bowl 2018 (challenge) |
| Metric | DSC |
| Unit | PERCENT |
| Direction | Higher is better |
| Results loaded | 3 of 3 |
| Coverage | All results currently returned for this benchmark |

Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol.

## Metrics

- [DSC](<https://www.sota2.com/research/sota/nuclei-segmentation-on-data-science-bowl-2018-challenge.md?metric=DSC>): Higher is better; unit: PERCENT.

- [mIoU](<https://www.sota2.com/research/sota/nuclei-segmentation-on-data-science-bowl-2018-challenge.md?metric=mIoU>): Higher is better; unit: PERCENT.

- [Recall](<https://www.sota2.com/research/sota/nuclei-segmentation-on-data-science-bowl-2018-challenge.md?metric=Recall>): Higher is better; unit: PERCENT.

- [Precision](<https://www.sota2.com/research/sota/nuclei-segmentation-on-data-science-bowl-2018-challenge.md?metric=Precision>): Higher is better; unit: PERCENT.

## Recorded results

| Method | DSC | Result date | Report paper |
| --- | --- | --- | --- |
| DoubleU-Net | 91.33 | Not recorded | 68fa67d41e209131cea18bd0 |
| UNet++ | 89.74 | Not recorded | 68fa67d41e209131cea18bd0 |
| U-Net | 75.73 | Not recorded | 68fa67d41e209131cea18bd0 |

### DoubleU-Net

DSC: 91.33. Result ID: 698d490b8b517eb433205b2b.

[Reporting paper](<https://www.sota2.com/research/paper/68fa67d41e209131cea18bd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa67d41e209131cea18bd0.md>)

[Implementation](<https://github.com/DebeshJha/2020-CBMS-DoubleU-Net>)

Recorded evaluation configuration: {"Pre-trained network":"VGG-19"}. This record does not assert independent reproduction.

### UNet++

DSC: 89.74. Result ID: 698d490b8b517eb433205b2a.

[Reporting paper](<https://www.sota2.com/research/paper/68fa67d41e209131cea18bd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa67001e209131cea13aa4.md>)

[Implementation](<https://github.com/MrGiovanni/UNetPlusPlus>)

Recorded evaluation configuration: {"Pre-trained network":"Resnet101"}. This record does not assert independent reproduction.

### U-Net

DSC: 75.73. Result ID: 698d490b8b517eb433205b29.

[Reporting paper](<https://www.sota2.com/research/paper/68fa67d41e209131cea18bd0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa67001e209131cea13aa4.md>)

[Implementation](<https://github.com/MrGiovanni/UNetPlusPlus>)

Recorded evaluation configuration: {"Pre-trained network":"Resnet101"}. This record does not assert independent reproduction.

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