---
title: Revvity Full Cell Segmentation Dataset
url: https://www.emergentmind.com/topics/revvity-full-cell-segmentation-dataset
type: topic
---

# Revvity Full Cell Segmentation Dataset

Searching arXiv for the dataset paper and closely related segmentation benchmarks.
The **2025 Revvity Full Cell Segmentation Dataset**, also referred to in experiments as **Revvity-25**, is a brightfield microscopy dataset for **cell instance segmentation** that was introduced together with **IAUNet**, a query-based U-Net architecture for biomedical instance segmentation [2508.01928]. It is defined by **detailed annotations of overlapping cell cytoplasm in brightfield images**, with a stated emphasis on **accurate and detailed annotations for cell borders and overlaps** [2508.01928]. The dataset is positioned as a benchmark for **cell instance segmentation**, and the paper further states that it **“opens new possibilities for testing and benchmarking models for modal and amodal semantic and instance segmentation”** [2508.01928].

## 1. Definition and scope

The dataset is presented as a distinct contribution in the IAUNet work: **“One of our key contributions in this paper is a novel cell instance segmentation dataset named Revvity-25”** [2508.01928]. Its stated role is to provide **detailed, expert-validated instance annotations** in **brightfield** microscopy, specifically for segmentation settings in which **overlapping cells** and **precise cell borders** are central difficulties [2508.01928].

The task formulation is **single-class instance segmentation** of **whole cells**, with the annotations targeting **cell cytoplasm** rather than nuclei alone. The paper characterizes the target structures as **“overlapping cell cytoplasm in brightfield images”** and emphasizes **“cell borders and overlaps”** [2508.01928]. No separate nucleus labels, multi-class labels, or cell-subtype labels are described. This suggests that the dataset is intended primarily for methods that must distinguish individual cell instances under partial contact or overlap, rather than for multi-structure parsing.

A plausible implication is that Revvity-25 occupies a niche distinct from nucleus-centric fluorescence datasets and from semantic-only cell masks. In the paper’s framing, its importance lies in the combination of brightfield imaging, whole-cell delineation, and high-detail manual contours [2508.01928].

## 2. Image content and dataset composition

The paper gives explicit dataset statistics: **“It includes 110 high-resolution 1080 × 1080 brightfield images, each containing, on average, 27 manually labeled and expert-validated cancer cells, totaling 2937 annotated cells”** [2508.01928]. The images therefore constitute a relatively compact but densely annotated benchmark.

The train-test partition is also explicit: **“The Revvity-25 dataset is divided equally into train and test sets, each containing 55 images”** [2508.01928]. No separate validation split is described in the paper. The biological material is described only as **cancer cells**; the paper does **not** specify the exact cancer cell line or tissue of origin [2508.01928].

The dataset’s core reported properties can be summarized as follows:

| Property | Reported value |
|---|---|
| Number of images | 110 |
| Image resolution | 1080 × 1080 |
| Average cells per image | 27 |
| Total annotated cells | 2937 |
| Train split | 55 images |
| Test split | 55 images |
| Imaging modality | Brightfield |

The paper does not specify objective magnification, numerical aperture, bit depth, file format, or acquisition hardware [2508.01928]. It also does not describe time series, multiple channels, or live-cell imaging. This suggests a static-image benchmark centered on high-resolution brightfield morphology.

The text further characterizes the morphology as challenging: **“the first dataset with accurate and detailed annotations for cell borders and overlaps, with each cell annotated using an average of 60 polygon points, reaching up to 400 points for more complex structures”** [2508.01928]. This indicates substantial contour complexity and implies that at least some objects exhibit irregular boundaries or heavy overlap.

## 3. Annotation design and label semantics

Revvity-25 is explicitly an **instance-level** dataset. The paper describes it as containing **“hundreds of carefully annotated cell instances in high-resolution brightfield images, each thoroughly hand-labeled and validated”** and reports a total of **2937 annotated cells** [2508.01928]. Each annotated object corresponds to a full cell extent, specifically the **cytoplasm**, rather than only a nucleus.

The annotation granularity is unusually fine for a brightfield benchmark. The paper states that each cell is annotated using **an average of 60 polygon points**, with up to **400 points for more complex structures** [2508.01928]. These are therefore high-density polygonal contours rather than coarse boxes or low-resolution masks. The stated motivation is accurate representation of **cell borders** and **overlaps**.

The annotation process is described as manual and quality-controlled: the cells are **“manually labeled and expert-validated”** and **“thoroughly hand-labeled and validated”** [2508.01928]. No inter-annotator agreement statistic is reported. The body text does not explicitly name the labeling tool, although the references include **Label Studio**, which strongly suggests that it was used in the annotation workflow [2508.01928]. Since the paper does not state this directly, that remains an inference.

The representation of overlap is central. The paper emphasizes **“precise annotation of cell borders, even in cases of overlapping cells, allowing it to capture complex cell interactions”** [2508.01928]. This suggests that each touching or overlapping cell is delineated as a separate instance, even where image evidence is locally ambiguous. The paper does not explicitly specify whether the release format is COCO polygons, per-instance masks, or another serialization, but because the experiments use Mask R-CNN, Mask2Former, MaskDINO, and IAUNet, a plausible implication is that the annotations are distributed in, or can be converted into, a COCO-like instance format [2508.01928].

## 4. Evaluation protocol and benchmark usage

The dataset is evaluated with **COCO-style average precision** metrics: **AP**, **AP\(_{50}\)**, **AP\(_{75}\)**, **AP\(_S\)**, **AP\(_M\)**, and **AP\(_L\)** [2508.01928]. The paper does not restate the formal AP definition, but its evaluation table uses the standard nomenclature and object-size stratification [2508.01928].

The experimental preprocessing protocol is given explicitly and applies to Revvity-25 as part of the broader IAUNet evaluation setup. During training, the images undergo **longest-side resizing** to **512 × 512 pixels**, preserving aspect ratio, followed by **scale jittering** within **0.8 to 1.5**, **fixed-size cropping to 512 × 512**, and **random flipping** [2508.01928]. During inference, the paper states that it applies **the same resizing process** and uses a consistent **mask prediction threshold of 0.5** across models [2508.01928]. It also specifies that comparisons use **single-scale inference** and models **trained until full convergence** [2508.01928].

These details matter because Revvity-25 images are originally **1080 × 1080**, whereas the reported benchmarks are obtained after standardized resizing and cropping. A plausible implication is that boundary fidelity is evaluated under a scale-normalized inference regime rather than at native resolution, which may affect how methods exploit the high polygon detail present in the annotations.

## 5. Performance results and baseline status

The Revvity-25 benchmark is used to compare IAUNet against convolution-based, transformer-based, and query-based baselines, including **Mask R-CNN**, **PointRend**, **Mask2Former**, and **MaskDINO** [2508.01928]. The paper’s caption summarizes the outcome: **“IAUNet outperforms strong query-based Mask2Former and MaskDINO baselines as well as other state-of-the-art models when training with fewer parameters. … IAUNet also efficiently scales with more queries while remaining efficient”** [2508.01928].

Selected reported results on Revvity-25 are as follows:

| Model / backbone | AP | AP\(_{50}\) | AP\(_{75}\) |
|---|---:|---:|---:|
| Mask R-CNN / R50 | 39.7 | 77.2 | 37.4 |
| Mask2Former / R50 | 46.4 | 79.8 | 49.9 |
| MaskDINO / R50 | 45.6 | 80.4 | 48.2 |
| IAUNet / R50 | 49.7 | 82.1 | 54.8 |
| Mask2Former / Swin-B | 52.0 | 83.6 | 58.4 |
| IAUNet / Swin-B | 53.5 | 86.1 | 59.4 |
| IAUNet / Swin-B, 300 queries | 53.7 | 86.5 | 59.4 |

The full table also reports **AP\(_S\)**, **AP\(_M\)**, **AP\(_L\)**, parameter counts, and FLOPs [2508.01928]. In the IAUNet paper, the strongest reported Revvity-25 result is **AP = 53.7** with **Swin-B** and **300 queries** [2508.01928].

The accompanying textual interpretation is that **“IAUNet consistently outperforms other state-of-the-art models”** on image-wise AP visualization and **“visibly offers more detailed segmentation, capturing longer pixel relationships and effectively handling overlapping regions in some cases”** [2508.01928]. Within the article’s scope, Revvity-25 therefore functions not only as a dataset release but also as a stress test for architectures designed to separate overlapping brightfield cell instances.

## 6. Relation to other cell-segmentation datasets

The IAUNet paper places Revvity-25 alongside **LIVECell**, **EVICAN2**, and **ISBI2014** [2508.01928]. In that comparison, Revvity-25 is distinguished by three features repeatedly emphasized in the text: it is **brightfield**, it targets **full-cell cytoplasm** rather than only nuclei, and it prioritizes **detailed annotations for cell borders and overlaps** [2508.01928].

The paper’s comparison can be summarized in conceptual terms. LIVECell is associated with **phase-contrast** imaging and very large scale; EVICAN2 spans **brightfield + phase contrast + fluorescence**; ISBI2014 is a more specialized cytology benchmark [2508.01928]. Revvity-25 is presented instead as a benchmark specifically for **brightfield cancer cell images** with high-detail **cytoplasm contours** and overlap-aware labeling [2508.01928].

The paper further claims: **“To our knowledge, this is the first dataset with accurate and detailed annotations for cell borders and overlaps”** [2508.01928]. This is a strong positioning statement, but it should be understood exactly as given in the source. The paper does not claim that no prior whole-cell or overlap-rich datasets exist; rather, it claims novelty in the combination of **accurate and detailed annotations** for **cell borders and overlaps** in this setting [2508.01928].

A plausible implication is that Revvity-25 is particularly relevant for evaluating **modal and amodal** segmentation formulations. The paper explicitly mentions this broader benchmarking role, even though it does not describe separate amodal labels or occlusion masks [2508.01928].

## 7. Access, usage considerations, and interpretive boundaries

The dataset is reported as available via the IAUNet project repository: **“Dataset available at: https://github.com/SlavkoPrytula/IAUNet”** [2508.01928]. The same repository is also the stated code release location for IAUNet [2508.01928]. No DOI or dedicated standalone data portal is provided in the paper.

The paper does not specify license terms, directory structure, exact file naming conventions, or the on-disk annotation schema [2508.01928]. It also does not detail acquisition metadata such as microscope model or bit depth. Consequently, some practical aspects of reuse must be checked directly in the repository rather than inferred from the article text.

From the evidence given, Revvity-25 is best understood as a **single-class brightfield whole-cell instance segmentation benchmark** with the following defining characteristics: **110 high-resolution 1080 × 1080 images**, **2937 expert-validated cell instances**, **55/55 train-test split**, and **high-detail polygon annotations** averaging **60 points per cell** and reaching **400 points** for complex structures [2508.01928]. Its principal research significance lies in the explicit treatment of **overlapping cell cytoplasm**, the precision of its contour annotation, and its use as a benchmark where strong modern instance segmentation baselines can be compared under a standardized protocol [2508.01928].

Source: https://www.emergentmind.com/topics/revvity-full-cell-segmentation-dataset