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MahmoodLab/
CARTA

carta

tissue-microarray

multiplex-immunofluorescence

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CARTA: Core and Tissue Detection weights

[!NOTE] Plese check CORAL, a companion repo for spatial proteomics processing.

CARTA’s tissue model is a DeepLabV3-ResNet50 segmenter for tissue vs background in multiplex immunofluorescence nuclear images. It was trained on annotated data primarily CODEX and is intended for fluorescence multiplex IF tissue segmentation It has been tested on: CyCIF, CODEX, Keyence, Orion, Vectra, PhenoCycler.

Pretrained weights for CARTA (TMA core detection + tissue segmentation for multiplex immunofluorescence).

File	Task	Architecture	Size
`carta_core.pt`	TMA core bounding boxes	YOLOv8n	~6 MB
`carta_tissue.pt`	Tissue vs background	DeepLabV3-ResNet50	~161 MB

Usage

Weights download automatically when you install CARTA and run the pipeline:

python

from segmenter.weights import resolve_weights
detector = resolve_weights("detector")
segmenter = resolve_weights("segmenter")

Or explicitly:

python

from huggingface_hub import hf_hub_download
detector = hf_hub_download("MahmoodLab/CARTA", "carta_core.pt")
segmenter = hf_hub_download("MahmoodLab/CARTA", "carta_tissue.pt")

Detector (YOLO)

Input: 1280×1280 letterboxed nuclear RGB canvas
Default inference: conf=0.25, iou=0.7, agnostic_nms=True

Segmenter (DeepLab)

Internal compute: 1 µm/px (native crops resampled, masks upsampled to full resolution)
Tiled inference: 512×512, per-tile percentile normalization
Default export: DAPI + binary mask + GeoJSON per core

Intended use

Tested on: CyCIF, CODEX, Keyence, Orion, Vectra, PhenoCycler.

License

The CARTA weights are released under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. They are provided for non-commercial academic research only; commercial use, or the distribution of derivative models, requires prior approval.

Citation

Please cite the KRONOS paper:

bibtex

@article{shaban2024foundation,
  title        = {A Foundation Model for Spatial Proteomics},
  author       = {Muhammad Shaban and Yuzhou Chang and Huaying Qiu and Yao Yu Yeo and Andrew H. Song and Guillaume Jaume and Yuchen Wang and Luca L. Weishaupt and Tong Ding and Anurag Vaidya and Abdallah Lamane and Daniel Shao and Mohammed Zidane and Yunhao Bai and Paige McCallum and Shuli Luo and Wenrui Wu and Yang Wang and Precious Cramer and Chi Ngai Chan and Pierre Stephan and Johanna Schaffenrath and Jia Le Lee and Hendrik A Michel and Caiwei Tian and Cristina Almagro-Perez and Sophia J. Wagner and Sharifa Sahai and Ming Y. Lu and Richard J. Chen and Andrew Zhang and Mark Edward M Gonzales and Ahmad Makky and Joey Lee and Hao Cheng and Maximilian Haist and Darci Phillips and Yuqi Tan and Garry P Nolan and W. Richard Burack and Jacob D Estes and Jonathan T.C. Liu and Toni K Choueiri and Neeraj Agarwal and Marc Barry and Scott J Rodig and Long Phi Le and Georg Gerber and Christian M. Schürch and Fabian J. Theis and Youn H Kim and Joe Yeong and Sabina Signoretti and Brooke Howitt and Lit-Hsin Loo and Qin Ma and Sizun Jiang and Faisal Mahmood},
  year         = {2025},
  note         = {Preprint},
  howpublished = {\url{https://arxiv.org/abs/2506.03373}},
}