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We developed a suite of pre-trained 3D models, named SuPreM, that combined the best of large-scale datasets and per-voxel annotations, showing the transferability across a range of 3D medical imaging tasks.

Paper

Transitioning to Fully-Supervised Pre-Training with Large-Scale Radiology ImageNet for Improved AI Transferability in Three-Dimensional Medical Segmentation
Wenxuan Li¹, Junfei Xiao¹, Jie Liu², Yucheng Tang³, Alan Yuille¹, and Zongwei Zhou^1,*
1Johns Hopkins University
²City University of Hong Kong
³NVIDIA
Radiological Society of North America (RSNA) 2023
abstract | code | slides | talk

An Extensive Dataset: AbdomenAtlas 1.1

The release of AbdomenAtlas 1.0 can be found at https://github.com/MrGiovanni/AbdomenAtlas

AbdomenAtlas 1.1 is an extensive dataset of 9,262 CT volumes with per-voxel annotation of 25 organs and pseudo annotations for seven types of tumors, enabling us to finally perform supervised pre-training of AI models at scale. Based on AbdomenAtlas 1.1, we also provide a suite of pre-trained models comprising several widely recognized AI models.

Prelimianry benchmark showed that supervised pre-training strikes as a preferred choice in terms of performance and efficiency compared with self-supervised pre-training.

We anticipate that the release of large, annotated datasets (AbdomenAtlas 1.1) and the suite of pre-trained models (SuPreM) will bolster collaborative endeavors in establishing Foundation Datasets and Foundation Models for the broader applications of 3D volumetric medical image analysis.

A Suite of Pre-trained Models: SuPreM

The following is a list of supported model backbones in our collection. Select the appropriate family of backbones and click to expand the table, download a specific backbone and its pre-trained weights (name and download), and save the weights into ./pretrained_weights/. More backbones will be added along time. Please suggest the backbone in this channel if you want us to pre-train it on AbdomenAtlas 1.1 containing 9,262 annotated CT volumes.

Swin UNETR

name	params	pre-trained data	resources	download
Tang et al.	62.19M	5050 CT		weights
Jose Valanaras et al.	62.19M	50000 CT/MRI		weights
Universal Model	62.19M	2100 CT		weights
SuPreM	62.19M	2100 CT	ours 🌟	weights

U-Net

name	params	pre-trained data	resources	download
Models Genesis	19.08M	623 CT		weights
UniMiSS	tiny	5022 CT&MRI		weights
	small			weights
Med3D	85.75M	1638 CT		weights
DoDNet	17.29M	920 CT		weights
Universal Model	19.08M	2100 CT		weights

SegResNet

name	params	pre-trained data	resources	download
SuPreM	62.19M	2100 CT	ours 🌟	weights

Acknowledgement

This work was supported by the Lustgarten Foundation for Pancreatic Cancer Research and the McGovern Foundation. The segmentation backbone is based on Swin UNETR; we appreciate the effort of the MONAI Team to provide and maintain open-source code to the community. Paper content is covered by patents pending.