UKBOB: One Billion MRI Labeled Masks for Generalizable 3D Medical Image
  Segmentation
Authors: name: Emmanuelle Bourigault, name: Amir Jamaludin, name: Abdullah Hamdi

In medical imaging, the primary challenge is collecting large-scale labeled
data due to privacy concerns, logistics, and high labeling costs. In this work,
we present the UK Biobank Organs and Bones (UKBOB), the largest labeled dataset
of body organs, comprising 51,761 MRI 3D samples (equivalent to 17.9 million 2D
images) and more than 1.37 billion 2D segmentation masks of 72 organs, all
based on the UK Biobank MRI dataset. We utilize automatic labeling, introduce
an automated label cleaning pipeline with organ-specific filters, and manually
annotate a subset of 300 MRIs with 11 abdominal classes to validate the quality
(referred to as UKBOB-manual). This approach allows for scaling up the dataset
collection while maintaining confidence in the labels. We further confirm the
validity of the labels by demonstrating zero-shot generalization of trained
models on the filtered UKBOB to other small labeled datasets from similar
domains (e.g., abdominal MRI). To further mitigate the effect of noisy labels,
we propose a novel method called Entropy Test-time Adaptation (ETTA) to refine
the segmentation output. We use UKBOB to train a foundation model, Swin-BOB,
for 3D medical image segmentation based on the Swin-UNetr architecture,
achieving state-of-the-art results in several benchmarks in 3D medical imaging,
including the BRATS brain MRI tumor challenge (with a 0.4% improvement) and the
BTCV abdominal CT scan benchmark (with a 1.3% improvement). The pre-trained
models and the code are available at https://emmanuelleb985.github.io/ukbob ,
and the filtered labels will be made available with the UK Biobank.