MedVision v1.2.0 redistribution (squashed history)

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README.md

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+---
+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, 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_Task02_Axial_Test`
+  - `DEEP-PSMA_BoxSize_Task02_Axial_Train`
+  - `DEEP-PSMA_MaskSize_Task01_Axial_Test`
+  - `DEEP-PSMA_MaskSize_Task01_Axial_Train`
+  - `DEEP-PSMA_MaskSize_Task02_Axial_Test`
+  - `DEEP-PSMA_MaskSize_Task02_Axial_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")
+```
+

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