相关论文: Deep Learning Segmentation of Spiral Arms and Bars

This paper presents novel methods to predict the surface and volume of the ham through a camera. This implies that the conventional weight measurement to obtain in the object's volume can be neglected and hence it is economically effective.…

We investigate how the properties of spiral arms relate to other fundamental galaxy properties. To this end, we use previously published measurements of those properties, and our own measurements of arm-interarm luminosity contrasts for a…

Advances in image registration and machine learning have recently enabled volumetric analysis of postmortem brain tissue from conventional photographs of coronal slabs, which are routinely collected in brain banks and neuropathology…

Studying star formation in spiral arms tells us not only about the evolution of star formation, and molecular clouds, but can also tell us about the nature of spiral structure in galaxies. I will address both these topics using the results…

In contrast to the human vision that mainly depends on the shape for recognizing the objects, deep image recognition models are widely known to be biased toward texture. Recently, Meta research team has released the first foundation model…

Semantic segmentation in cataract surgery has a wide range of applications contributing to surgical outcome enhancement and clinical risk reduction. However, the varying issues in segmenting the different relevant structures in these…

Accurate identification of breast masses is crucial in diagnosing breast cancer; however, it can be challenging due to their small size and being camouflaged in surrounding normal glands. Worse still, it is also expensive in clinical…

According to one version of the recently proposed "manifold" theory that explains the origin of spirals and rings in relation to chaotic orbits, galaxies with stronger bars should have a higher spiral arms pitch angle when compared to…

Instance segmentation of unknown objects from images is regarded as relevant for several robot skills including grasping, tracking and object sorting. Recent results in computer vision have shown that large hand-labeled datasets enable high…

We propose a novel point cloud based 3D organ segmentation pipeline utilizing deep Q-learning. In order to preserve shape properties, the learning process is guided using a statistical shape model. The trained agent directly predicts…

Spiral structure is ubiquitous in the Universe, and the pitch angle of arms in spiral galaxies provide an important observable in efforts to discriminate between different mechanisms of spiral arm formation and evolution. In this paper, we…

Cardio-cerebrovascular diseases are the leading causes of mortality worldwide, whose accurate blood vessel segmentation is significant for both scientific research and clinical usage. However, segmenting cardio-cerebrovascular structures…

The success of deep convolutional neural networks on image classification and recognition tasks has led to new applications in very diversified contexts, including the field of medical imaging. In this paper we investigate and propose…

We developed an algorithm to use Galaxy Zoo 3D spiral arm masks produced by citizen scientist volunteers to semi-automatically classify spiral galaxies as either multi-armed or grand design spirals. Our final sample consists of 299…

Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly…

With the use of a background Milky-Way-like potential model, we performed stellar orbital and magnetohydrodynamic (MHD) simulations. As a first experiment, we studied the gaseous response to a bisymmetric spiral arm potential: the widely…

The spiral structure in the Solar neighborhood is an important issue in astronomy. In the past few years, there is significant progress in observation. The distances for a large number of good spiral tracers, i.e. giant molecular clouds,…

Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies…

Pre-trained segmentation models are a powerful and flexible tool for segmenting images. Recently, this trend has extended to medical imaging. Yet, often these methods only produce a single prediction for a given image, neglecting inherent…

Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring. Recently, deep convolutional neural network (DCNNs) has obtained tremendous success in 2D and 3D medical image segmentation.…