---
license: cc-by-nc-4.0
task_categories:
- image-segmentation
language:
- en
tags:
- medical
- image
- pet
- prostate
- cancer
- segmentation
pretty_name: 'deep-psma-lite'
size_categories:
- n<1K
---


## About
This is a preprocessed redistribution of [DEEP-PSMA](https://deep-psma.grand-challenge.org/) ([Zenodo](https://zenodo.org/records/15281784)), which is released under the `CC BY-NC 4.0` license.

**Dataset summary:** 100 cases with paired PSMA and FDG PET scans and total-tumour-burden (TTB) masks for each tracer.

**Contents of this repository:**

- `Images-PSMA/` — 100 files
- `Masks-PSMA/` — 100 files
- `Images-FDG/` — 100 files
- `Masks-FDG/` — 100 files

📝 Landmark annotations, visualization figures and the benchmark plan files live in 🔥[MedVision](https://huggingface.co/datasets/YongchengYAO/MedVision)🔥, where you can load the complete images and annotations from dataset configs.


## Relation to the source dataset

| | |
| --- | --- |
| In the source | 100 cases, each with PET + CT + `totseg_24` for BOTH the PSMA and FDG tracers |
| Excluded here | the CT and `totseg_24` volumes for both tracers |
| **In this repo** | **100 `Images-PSMA` + 100 `Masks-PSMA` + 100 `Images-FDG` + 100 `Masks-FDG`** |

All **100 cases** and **both tracers** are included, but the **CT** and `totseg_24` volumes are not redistributed. The total-tumour-burden annotation is defined by SUV thresholding on the **PET** and is delivered on the PET grid (e.g. `192x192x335` at `2.87 x 2.87 x 3.27` mm). The CT of a PET/CT is acquired at roughly 1 mm for attenuation correction, so using it as the image would require resampling the mask onto a ~3x finer grid — inventing lesion boundary detail that was never annotated and changing the physical measurements.

**Why `-Lite`?** The suffix marks this as a *derived* redistribution rather than a copy of the source. These are **preprocessed** volumes — every case has been format-converted where needed, geometry-normalised and reoriented to RAS+ — and for some sources cases or modalities are excluded as well (see the table above). Use it to reproduce MedVision, not as a substitute for the original release. See [Preprocessing](#preprocessing) below for exactly what was changed.


## Preprocessing

- PET (SUV) volumes and their TTB masks converted to `nii.gz` and standardized to RAS+ orientation.

- The two tracers are kept in **separate** image/mask folders so that the subject-level train/test split cannot place the same patient's PSMA and FDG scans on opposite sides.


## Segmentation Labels

```python
labels_map = {
    "1": "total tumor burden"
}
```


## Landmarks

```python
landmarks_map = {
    "P1": "most right/anterior/superior endpoint of the major axis",
    "P2": "most left/superior/inferior endpoint of the major axis",
    "P3": "most right/anterior/superior endpoint of the minor axis",
    "P4": "most left/superior/inferior endpoint of the minor axis"
}
```


## News
- [25 Jul, 2026] Initial release. This dataset is integrated into 🔥[MedVision](https://huggingface.co/datasets/YongchengYAO/MedVision)🔥, where you can use these config names to load data in python:

  - `DEEP-PSMA_BoxSize_Task01_Axial_Test`
  - `DEEP-PSMA_BoxSize_Task01_Axial_Train`
  - `DEEP-PSMA_BoxSize_Task01_Coronal_Test`
  - `DEEP-PSMA_BoxSize_Task01_Coronal_Train`
  - `DEEP-PSMA_BoxSize_Task01_Sagittal_Test`
  - `DEEP-PSMA_BoxSize_Task01_Sagittal_Train`
  - `DEEP-PSMA_BoxSize_Task02_Axial_Test`
  - `DEEP-PSMA_BoxSize_Task02_Axial_Train`
  - `DEEP-PSMA_BoxSize_Task02_Coronal_Test`
  - `DEEP-PSMA_BoxSize_Task02_Coronal_Train`
  - `DEEP-PSMA_BoxSize_Task02_Sagittal_Test`
  - `DEEP-PSMA_BoxSize_Task02_Sagittal_Train`
  - `DEEP-PSMA_MaskSize_Task01_Axial_Test`
  - `DEEP-PSMA_MaskSize_Task01_Axial_Train`
  - `DEEP-PSMA_MaskSize_Task01_Coronal_Test`
  - `DEEP-PSMA_MaskSize_Task01_Coronal_Train`
  - `DEEP-PSMA_MaskSize_Task01_Sagittal_Test`
  - `DEEP-PSMA_MaskSize_Task01_Sagittal_Train`
  - `DEEP-PSMA_MaskSize_Task02_Axial_Test`
  - `DEEP-PSMA_MaskSize_Task02_Axial_Train`
  - `DEEP-PSMA_MaskSize_Task02_Coronal_Test`
  - `DEEP-PSMA_MaskSize_Task02_Coronal_Train`
  - `DEEP-PSMA_MaskSize_Task02_Sagittal_Test`
  - `DEEP-PSMA_MaskSize_Task02_Sagittal_Train`
  - `DEEP-PSMA_TumorLesionSize_Task01_Axial_Test`
  - `DEEP-PSMA_TumorLesionSize_Task01_Axial_Train`
  - `DEEP-PSMA_TumorLesionSize_Task02_Axial_Test`
  - `DEEP-PSMA_TumorLesionSize_Task02_Axial_Train`


## Data Usage Agreement
By using the dataset, you agree to the terms as follow.
- You must comply with the original `CC BY-NC 4.0` license terms of the source dataset.
- You are recommended to refer to the source of this dataset in any publication: `https://huggingface.co/datasets/YongchengYAO/DEEP-PSMA-Lite`
- You must cite the original publication(s):
  - https://doi.org/10.5281/zenodo.15281784


## Official Release
For more information, please go to the official site: https://deep-psma.grand-challenge.org/


## Download from Huggingface
```python
# python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="YongchengYAO/DEEP-PSMA-Lite", repo_type='dataset', local_dir="/your/local/folder")
```