1,734 research outputs found
C2FTrans: Coarse-to-Fine Transformers for Medical Image Segmentation
Convolutional neural networks (CNN), the most prevailing architecture for
deep-learning based medical image analysis, are still functionally limited by
their intrinsic inductive biases and inadequate receptive fields. Transformer,
born to address this issue, has drawn explosive attention in natural language
processing and computer vision due to its remarkable ability in capturing
long-range dependency. However, most recent transformer-based methods for
medical image segmentation directly apply vanilla transformers as an auxiliary
module in CNN-based methods, resulting in severe detail loss due to the rigid
patch partitioning scheme in transformers. To address this problem, we propose
C2FTrans, a novel multi-scale architecture that formulates medical image
segmentation as a coarse-to-fine procedure. C2FTrans mainly consists of a
cross-scale global transformer (CGT) which addresses local contextual
similarity in CNN and a boundary-aware local transformer (BLT) which overcomes
boundary uncertainty brought by rigid patch partitioning in transformers.
Specifically, CGT builds global dependency across three different small-scale
feature maps to obtain rich global semantic features with an acceptable
computational cost, while BLT captures mid-range dependency by adaptively
generating windows around boundaries under the guidance of entropy to reduce
computational complexity and minimize detail loss based on large-scale feature
maps. Extensive experimental results on three public datasets demonstrate the
superior performance of C2FTrans against state-of-the-art CNN-based and
transformer-based methods with fewer parameters and lower FLOPs. We believe the
design of C2FTrans would further inspire future work on developing efficient
and lightweight transformers for medical image segmentation. The source code of
this paper is publicly available at https://github.com/xianlin7/C2FTrans
SAMUS: Adapting Segment Anything Model for Clinically-Friendly and Generalizable Ultrasound Image Segmentation
Segment anything model (SAM), an eminent universal image segmentation model,
has recently gathered considerable attention within the domain of medical image
segmentation. Despite the remarkable performance of SAM on natural images, it
grapples with significant performance degradation and limited generalization
when confronted with medical images, particularly with those involving objects
of low contrast, faint boundaries, intricate shapes, and diminutive sizes. In
this paper, we propose SAMUS, a universal model tailored for ultrasound image
segmentation. In contrast to previous SAM-based universal models, SAMUS pursues
not only better generalization but also lower deployment cost, rendering it
more suitable for clinical applications. Specifically, based on SAM, a parallel
CNN branch is introduced to inject local features into the ViT encoder through
cross-branch attention for better medical image segmentation. Then, a position
adapter and a feature adapter are developed to adapt SAM from natural to
medical domains and from requiring large-size inputs (1024x1024) to small-size
inputs (256x256) for more clinical-friendly deployment. A comprehensive
ultrasound dataset, comprising about 30k images and 69k masks and covering six
object categories, is collected for verification. Extensive comparison
experiments demonstrate SAMUS's superiority against the state-of-the-art
task-specific models and universal foundation models under both task-specific
evaluation and generalization evaluation. Moreover, SAMUS is deployable on
entry-level GPUs, as it has been liberated from the constraints of long
sequence encoding. The code, data, and models will be released at
https://github.com/xianlin7/SAMUS
Bis[4-(2-hyÂdroxyÂbenzylÂideneÂamino)Âbenzoato-κO 1]tetraÂkisÂ(methanol-κO)cadmium
In the title mononuclear complex, [Cd(C14H10NO3)2(CH3OH)4], the Cd2+ cation is situated on an inversion centre. It exhibits a distorted octaÂhedral coordination, defined by two carboxylÂate O atoms from two monodentate anions and by four O atoms from four methanol molÂecules. The crystal structure comprises intraÂmolecular O—H⋯O and O—H⋯N, and interÂmolecular O—H⋯O hydrogen bonds. The latter help to construct a layered structure extending parallel to (100)
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