8 research outputs found
Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation
Accurate segmentation of the optic disc (OD) and cup (OC)in fundus images
from different datasets is critical for glaucoma disease screening. The
cross-domain discrepancy (domain shift) hinders the generalization of deep
neural networks to work on different domain datasets.In this work, we present
an unsupervised domain adaptation framework,called Boundary and Entropy-driven
Adversarial Learning (BEAL), to improve the OD and OC segmentation performance,
especially on the ambiguous boundary regions. In particular, our proposed BEAL
frame-work utilizes the adversarial learning to encourage the boundary
prediction and mask probability entropy map (uncertainty map) of the target
domain to be similar to the source ones, generating more accurate boundaries
and suppressing the high uncertainty predictions of OD and OC segmentation. We
evaluate the proposed BEAL framework on two public retinal fundus image
datasets (Drishti-GS and RIM-ONE-r3), and the experiment results demonstrate
that our method outperforms the state-of-the-art unsupervised domain adaptation
methods. Codes will be available at https://github.com/EmmaW8/BEAL.Comment: Accepted at MICCAI 201
Distribution Aligned Diffusion and Prototype-guided network for Unsupervised Domain Adaptive Segmentation
The Diffusion Probabilistic Model (DPM) has emerged as a highly effective
generative model in the field of computer vision. Its intermediate latent
vectors offer rich semantic information, making it an attractive option for
various downstream tasks such as segmentation and detection. In order to
explore its potential further, we have taken a step forward and considered a
more complex scenario in the medical image domain, specifically, under an
unsupervised adaptation condition. To this end, we propose a Diffusion-based
and Prototype-guided network (DP-Net) for unsupervised domain adaptive
segmentation. Concretely, our DP-Net consists of two stages: 1) Distribution
Aligned Diffusion (DADiff), which involves training a domain discriminator to
minimize the difference between the intermediate features generated by the DPM,
thereby aligning the inter-domain distribution; and 2) Prototype-guided
Consistency Learning (PCL), which utilizes feature centroids as prototypes and
applies a prototype-guided loss to ensure that the segmentor learns consistent
content from both source and target domains. Our approach is evaluated on
fundus datasets through a series of experiments, which demonstrate that the
performance of the proposed method is reliable and outperforms state-of-the-art
methods. Our work presents a promising direction for using DPM in complex
medical image scenarios, opening up new possibilities for further research in
medical imaging
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation
In the domain adaptation problem, source data may be unavailable to the
target client side due to privacy or intellectual property issues. Source-free
unsupervised domain adaptation (SF-UDA) aims at adapting a model trained on the
source side to align the target distribution with only the source model and
unlabeled target data. The source model usually produces noisy and
context-inconsistent pseudo-labels on the target domain, i.e., neighbouring
regions that have a similar visual appearance are annotated with different
pseudo-labels. This observation motivates us to refine pseudo-labels with
context relations. Another observation is that features of the same class tend
to form a cluster despite the domain gap, which implies context relations can
be readily calculated from feature distances. To this end, we propose a
context-aware pseudo-label refinement method for SF-UDA. Specifically, a
context-similarity learning module is developed to learn context relations.
Next, pseudo-label revision is designed utilizing the learned context
relations. Further, we propose calibrating the revised pseudo-labels to
compensate for wrong revision caused by inaccurate context relations.
Additionally, we adopt a pixel-level and class-level denoising scheme to select
reliable pseudo-labels for domain adaptation. Experiments on cross-domain
fundus images indicate that our approach yields the state-of-the-art results.
Code is available at https://github.com/xmed-lab/CPR.Comment: Accepted by MICCAI 2023, 11 page
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
The Segment Anything Model (SAM) has recently gained popularity in the field
of image segmentation. Thanks to its impressive capabilities in all-round
segmentation tasks and its prompt-based interface, SAM has sparked intensive
discussion within the community. It is even said by many prestigious experts
that image segmentation task has been "finished" by SAM. However, medical image
segmentation, although an important branch of the image segmentation family,
seems not to be included in the scope of Segmenting "Anything". Many individual
experiments and recent studies have shown that SAM performs subpar in medical
image segmentation. A natural question is how to find the missing piece of the
puzzle to extend the strong segmentation capability of SAM to medical image
segmentation. In this paper, instead of fine-tuning the SAM model, we propose
Med SAM Adapter, which integrates the medical specific domain knowledge to the
segmentation model, by a simple yet effective adaptation technique. Although
this work is still one of a few to transfer the popular NLP technique Adapter
to computer vision cases, this simple implementation shows surprisingly good
performance on medical image segmentation. A medical image adapted SAM, which
we have dubbed Medical SAM Adapter (MSA), shows superior performance on 19
medical image segmentation tasks with various image modalities including CT,
MRI, ultrasound image, fundus image, and dermoscopic images. MSA outperforms a
wide range of state-of-the-art (SOTA) medical image segmentation methods, such
as nnUNet, TransUNet, UNetr, MedSegDiff, and also outperforms the fully
fine-turned MedSAM with a considerable performance gap. Code will be released
at: https://github.com/WuJunde/Medical-SAM-Adapter
Multi Task Consistency Guided Source-Free Test-Time Domain Adaptation Medical Image Segmentation
Source-free test-time adaptation for medical image segmentation aims to
enhance the adaptability of segmentation models to diverse and previously
unseen test sets of the target domain, which contributes to the
generalizability and robustness of medical image segmentation models without
access to the source domain. Ensuring consistency between target edges and
paired inputs is crucial for test-time adaptation. To improve the performance
of test-time domain adaptation, we propose a multi task consistency guided
source-free test-time domain adaptation medical image segmentation method which
ensures the consistency of the local boundary predictions and the global
prototype representation. Specifically, we introduce a local boundary
consistency constraint method that explores the relationship between tissue
region segmentation and tissue boundary localization tasks. Additionally, we
propose a global feature consistency constraint toto enhance the intra-class
compactness. We conduct extensive experiments on the segmentation of benchmark
fundus images. Compared to prediction directly by the source domain model, the
segmentation Dice score is improved by 6.27\% and 0.96\% in RIM-ONE-r3 and
Drishti GS datasets, respectively. Additionally, the results of experiments
demonstrate that our proposed method outperforms existing competitive domain
adaptation segmentation algorithms.Comment: 31 pages,7 figure