4,439 research outputs found
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
Semantic segmentation is the pixel-wise labelling of an image. Since the
problem is defined at the pixel level, determining image class labels only is
not acceptable, but localising them at the original image pixel resolution is
necessary. Boosted by the extraordinary ability of convolutional neural
networks (CNN) in creating semantic, high level and hierarchical image
features; excessive numbers of deep learning-based 2D semantic segmentation
approaches have been proposed within the last decade. In this survey, we mainly
focus on the recent scientific developments in semantic segmentation,
specifically on deep learning-based methods using 2D images. We started with an
analysis of the public image sets and leaderboards for 2D semantic
segmantation, with an overview of the techniques employed in performance
evaluation. In examining the evolution of the field, we chronologically
categorised the approaches into three main periods, namely pre-and early deep
learning era, the fully convolutional era, and the post-FCN era. We technically
analysed the solutions put forward in terms of solving the fundamental problems
of the field, such as fine-grained localisation and scale invariance. Before
drawing our conclusions, we present a table of methods from all mentioned eras,
with a brief summary of each approach that explains their contribution to the
field. We conclude the survey by discussing the current challenges of the field
and to what extent they have been solved.Comment: Updated with new studie
Skeleton-Guided Instance Separation for Fine-Grained Segmentation in Microscopy
One of the fundamental challenges in microscopy (MS) image analysis is
instance segmentation (IS), particularly when segmenting cluster regions where
multiple objects of varying sizes and shapes may be connected or even
overlapped in arbitrary orientations. Existing IS methods usually fail in
handling such scenarios, as they rely on coarse instance representations such
as keypoints and horizontal bounding boxes (h-bboxes). In this paper, we
propose a novel one-stage framework named A2B-IS to address this challenge and
enhance the accuracy of IS in MS images. Our approach represents each instance
with a pixel-level mask map and a rotated bounding box (r-bbox). Unlike
two-stage methods that use box proposals for segmentations, our method
decouples mask and box predictions, enabling simultaneous processing to
streamline the model pipeline. Additionally, we introduce a Gaussian skeleton
map to aid the IS task in two key ways: (1) It guides anchor placement,
reducing computational costs while improving the model's capacity to learn
RoI-aware features by filtering out noise from background regions. (2) It
ensures accurate isolation of densely packed instances by rectifying erroneous
box predictions near instance boundaries. To further enhance the performance,
we integrate two modules into the framework: (1) An Atrous Attention Block
(A2B) designed to extract high-resolution feature maps with fine-grained
multiscale information, and (2) A Semi-Supervised Learning (SSL) strategy that
leverages both labeled and unlabeled images for model training. Our method has
been thoroughly validated on two large-scale MS datasets, demonstrating its
superiority over most state-of-the-art approaches
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