21 research outputs found
Weakly Supervised Volumetric Image Segmentation with Deformed Templates
There are many approaches that use weak-supervision to train networks to
segment 2D images. By contrast, existing 3D approaches rely on full-supervision
of a subset of 2D slices of the 3D image volume. In this paper, we propose an
approach that is truly weakly-supervised in the sense that we only need to
provide a sparse set of 3D point on the surface of target objects, an easy task
that can be quickly done. We use the 3D points to deform a 3D template so that
it roughly matches the target object outlines and we introduce an architecture
that exploits the supervision provided by coarse template to train a network to
find accurate boundaries.
We evaluate the performance of our approach on Computed Tomography (CT),
Magnetic Resonance Imagery (MRI) and Electron Microscopy (EM) image datasets.
We will show that it outperforms a more traditional approach to
weak-supervision in 3D at a reduced supervision cost.Comment: 13 Page
Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence
Sparse labels have been attracting much attention in recent years. However,
the performance gap between weakly supervised and fully supervised salient
object detection methods is huge, and most previous weakly supervised works
adopt complex training methods with many bells and whistles. In this work, we
propose a one-round end-to-end training approach for weakly supervised salient
object detection via scribble annotations without pre/post-processing
operations or extra supervision data. Since scribble labels fail to offer
detailed salient regions, we propose a local coherence loss to propagate the
labels to unlabeled regions based on image features and pixel distance, so as
to predict integral salient regions with complete object structures. We design
a saliency structure consistency loss as self-consistent mechanism to ensure
consistent saliency maps are predicted with different scales of the same image
as input, which could be viewed as a regularization technique to enhance the
model generalization ability. Additionally, we design an aggregation module
(AGGM) to better integrate high-level features, low-level features and global
context information for the decoder to aggregate various information. Extensive
experiments show that our method achieves a new state-of-the-art performance on
six benchmarks (e.g. for the ECSSD dataset: F_\beta = 0.8995, E_\xi = 0.9079
and MAE = 0.0489$), with an average gain of 4.60\% for F-measure, 2.05\% for
E-measure and 1.88\% for MAE over the previous best method on this task. Source
code is available at http://github.com/siyueyu/SCWSSOD.Comment: Accepted by AAAI202