18 research outputs found
Learning Site-specific Styles for Multi-institutional Unsupervised Cross-modality Domain Adaptation
Unsupervised cross-modality domain adaptation is a challenging task in
medical image analysis, and it becomes more challenging when source and target
domain data are collected from multiple institutions. In this paper, we present
our solution to tackle the multi-institutional unsupervised domain adaptation
for the crossMoDA 2023 challenge. First, we perform unpaired image translation
to translate the source domain images to the target domain, where we design a
dynamic network to generate synthetic target domain images with controllable,
site-specific styles. Afterwards, we train a segmentation model using the
synthetic images and further reduce the domain gap by self-training. Our
solution achieved the 1st place during both the validation and testing phases
of the challenge. The code repository is publicly available at
https://github.com/MedICL-VU/crossmoda2023.Comment: crossMoDA 2023 challenge 1st place solutio
COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training
Deep learning models have demonstrated remarkable success in multi-organ
segmentation but typically require large-scale datasets with all organs of
interest annotated. However, medical image datasets are often low in sample
size and only partially labeled, i.e., only a subset of organs are annotated.
Therefore, it is crucial to investigate how to learn a unified model on the
available partially labeled datasets to leverage their synergistic potential.
In this paper, we systematically investigate the partial-label segmentation
problem with theoretical and empirical analyses on the prior techniques. We
revisit the problem from a perspective of partial label supervision signals and
identify two signals derived from ground truth and one from pseudo labels. We
propose a novel two-stage framework termed COSST, which effectively and
efficiently integrates comprehensive supervision signals with self-training.
Concretely, we first train an initial unified model using two ground
truth-based signals and then iteratively incorporate the pseudo label signal to
the initial model using self-training. To mitigate performance degradation
caused by unreliable pseudo labels, we assess the reliability of pseudo labels
via outlier detection in latent space and exclude the most unreliable pseudo
labels from each self-training iteration. Extensive experiments are conducted
on one public and three private partial-label segmentation tasks over 12 CT
datasets. Experimental results show that our proposed COSST achieves
significant improvement over the baseline method, i.e., individual networks
trained on each partially labeled dataset. Compared to the state-of-the-art
partial-label segmentation methods, COSST demonstrates consistent superior
performance on various segmentation tasks and with different training data
sizes
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation
Medical image segmentation is a critical task in medical image analysis. In
recent years, deep learning based approaches have shown exceptional performance
when trained on a fully-annotated dataset. However, data annotation is often a
significant bottleneck, especially for 3D medical images. Active learning (AL)
is a promising solution for efficient annotation but requires an initial set of
labeled samples to start active selection. When the entire data pool is
unlabeled, how do we select the samples to annotate as our initial set? This is
also known as the cold-start AL, which permits only one chance to request
annotations from experts without access to previously annotated data.
Cold-start AL is highly relevant in many practical scenarios but has been
under-explored, especially for 3D medical segmentation tasks requiring
substantial annotation effort. In this paper, we present a benchmark named
COLosSAL by evaluating six cold-start AL strategies on five 3D medical image
segmentation tasks from the public Medical Segmentation Decathlon collection.
We perform a thorough performance analysis and explore important open questions
for cold-start AL, such as the impact of budget on different strategies. Our
results show that cold-start AL is still an unsolved problem for 3D
segmentation tasks but some important trends have been observed. The code
repository, data partitions, and baseline results for the complete benchmark
are publicly available at https://github.com/MedICL-VU/COLosSAL.Comment: Accepted by MICCAI 202