230 research outputs found
Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio
The cardiothoracic ratio (CTR), a clinical metric of heart size in chest
X-rays (CXRs), is a key indicator of cardiomegaly. Manual measurement of CTR is
time-consuming and can be affected by human subjectivity, making it desirable
to design computer-aided systems that assist clinicians in the diagnosis
process. Automatic CTR estimation through chest organ segmentation, however,
requires large amounts of pixel-level annotated data, which is often
unavailable. To alleviate this problem, we propose an unsupervised domain
adaptation framework based on adversarial networks. The framework learns domain
invariant feature representations from openly available data sources to produce
accurate chest organ segmentation for unlabeled datasets. Specifically, we
propose a model that enforces our intuition that prediction masks should be
domain independent. Hence, we introduce a discriminator that distinguishes
segmentation predictions from ground truth masks. We evaluate our system's
prediction based on the assessment of radiologists and demonstrate the clinical
practicability for the diagnosis of cardiomegaly. We finally illustrate on the
JSRT dataset that the semi-supervised performance of our model is also very
promising.Comment: Accepted by MICCAI 201
Curriculum semi-supervised segmentation
This study investigates a curriculum-style strategy for semi-supervised CNN
segmentation, which devises a regression network to learn image-level
information such as the size of a target region. These regressions are used to
effectively regularize the segmentation network, constraining softmax
predictions of the unlabeled images to match the inferred label distributions.
Our framework is based on inequality constraints that tolerate uncertainties
with inferred knowledge, e.g., regressed region size, and can be employed for a
large variety of region attributes. We evaluated our proposed strategy for left
ventricle segmentation in magnetic resonance images (MRI), and compared it to
standard proposal-based semi-supervision strategies. Our strategy leverages
unlabeled data in more efficiently, and achieves very competitive results,
approaching the performance of full-supervision.Comment: Accepted as paper as MICCAI 2O1
CEmb-SAM: Segment Anything Model with Condition Embedding for Joint Learning from Heterogeneous Datasets
Automated segmentation of ultrasound images can assist medical experts with
diagnostic and therapeutic procedures. Although using the common modality of
ultrasound, one typically needs separate datasets in order to segment, for
example, different anatomical structures or lesions with different levels of
malignancy. In this paper, we consider the problem of jointly learning from
heterogeneous datasets so that the model can improve generalization abilities
by leveraging the inherent variability among datasets. We merge the
heterogeneous datasets into one dataset and refer to each component dataset as
a subgroup. We propose to train a single segmentation model so that the model
can adapt to each sub-group. For robust segmentation, we leverage recently
proposed Segment Anything model (SAM) in order to incorporate sub-group
information into the model. We propose SAM with Condition Embedding block
(CEmb-SAM) which encodes sub-group conditions and combines them with image
embeddings from SAM. The conditional embedding block effectively adapts SAM to
each image sub-group by incorporating dataset properties through learnable
parameters for normalization. Experiments show that CEmb-SAM outperforms the
baseline methods on ultrasound image segmentation for peripheral nerves and
breast cancer. The experiments highlight the effectiveness of Cemb-SAM in
learning from heterogeneous datasets in medical image segmentation tasks
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