Semantic Segmentation of Radio-Astronomical Images

Pino, Carmelo; Renato, Sortino,; Sciacca, Eva; Riggi, S.; Spampinato, Concetto

doi:10.1007/978-3-030-89691-1_38

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Our work serves as the first step toward the implementation of next-generation imaging pipeline methods in medical imaging workflows. It can be applied to multitask research in many fields, such as video coding 24 and radio astronomical observations, 25 allowing for the collaboration of up- and downstream tasks and mastery over compressed sensing data with the theoretical and empirical guarantees of visually intermediate checks and a human–machine-friendly dataflow. FSL represents a targeted evolution of our previous work toward all-in-one focused deployment.…”

Section: Discussionmentioning

confidence: 99%

Promoting fast MR imaging pipeline by full-stack AI

Wang,

Li,

et al. 2024

iScience

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Section: Discussionmentioning

confidence: 99%

Promoting fast MR imaging pipeline by full-stack AI

Wang,

Li,

et al. 2024

iScience

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“…To the best of our knowledge, no study provides an overview of deep learning models, especially relative to semantic segmentation ones, applied to radio-astronomical images. The only work exploring semantic segmentation in radio-astronomy is carried out in [53]. Thus, one of the main contributions of this work will be the standardized comparison of a significant number of state-of-the-art detection and segmentation approaches on a dataset composed of real images from several telescopes.…”

Section: Related Workmentioning

confidence: 99%

Radio astronomical images object detection and segmentation: A benchmark on deep learning methods

Renato¹,

Magro²,

Fiameni³

et al. 2023

Preprint

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In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being evaluated in the domain of radio astronomy. In particular, since radio astronomy is entering the Big Data era, with the advent of the largest telescope in the world -the Square Kilometre Array (SKA), the task of automatic object detection and instance segmentation is crucial for source finding and analysis. In this work, we explore the performance of the most affirmed deep learning approaches, applied to astronomical images obtained by radio interferometric instrumentation, to solve the task of automatic source detection. This is carried out by applying models designed to accomplish two different kinds of tasks: object detection and semantic segmentation. The goal is

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“…For example, human nodules are much larger objects than typical mRNA spots. As for the star enhancement method proposed by Sadr et al ( 25 ) , it is not suited for low-intensity spots ( 28 ) , which is a major concern in spot detection for smFISH images.…”

Section: Related Workmentioning

confidence: 99%

DeepSpot: A deep neural network for RNA spot enhancement in single-molecule fluorescence in-situ hybridization microscopy images

et al. 2022

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Detection of RNA spots in single-molecule fluorescence in-situ hybridization microscopy images remains a difficult task, especially when applied to large volumes of data. The variable intensity of RNA spots combined with the high noise level of the images often requires manual adjustment of the spot detection thresholds for each image. In this work, we introduce DeepSpot, a Deep Learning-based tool specifically designed for RNA spot enhancement that enables spot detection without the need to resort to image per image parameter tuning. We show how our method can enable downstream accurate spot detection. DeepSpot’s architecture is inspired by small object detection approaches. It incorporates dilated convolutions into a module specifically designed for context aggregation for small object and uses Residual Convolutions to propagate this information along the network. This enables DeepSpot to enhance all RNA spots to the same intensity, and thus circumvents the need for parameter tuning. We evaluated how easily spots can be detected in images enhanced with our method by testing DeepSpot on 20 simulated and 3 experimental datasets, and showed that accuracy of more than 97% is achieved. Moreover, comparison with alternative deep learning approaches for mRNA spot detection (deepBlink) indicated that DeepSpot provides more precise mRNA detection. In addition, we generated single-molecule fluorescence in-situ hybridization images of mouse fibroblasts in a wound healing assay to evaluate whether DeepSpot enhancement can enable seamless mRNA spot detection and thus streamline studies of localized mRNA expression in cells.

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Semantic Segmentation of Radio-Astronomical Images

Cited by 8 publications

References 25 publications

Promoting fast MR imaging pipeline by full-stack AI

Promoting fast MR imaging pipeline by full-stack AI

Radio astronomical images object detection and segmentation: A benchmark on deep learning methods

DeepSpot: A deep neural network for RNA spot enhancement in single-molecule fluorescence in-situ hybridization microscopy images

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