Using the Polar Transform for Efficient Deep Learning-Based Aorta Segmentation in CTA Images

Benčević, Marin; Habijan, Marija; Galić, Irena; Babin, Danilo

doi:10.1109/elmar55880.2022.9899786

Cited by 4 publications

(2 citation statements)

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“…Examples include the segmentation of brain tumors in MRI [152], lung nodules in chest CT scans [153], polyps [154], and vessel delineation [155]. Additionally, they find widespread use in cardiovascular image segmentation tasks, encompassing the isolation of specific structures like the aorta [156,157], heart chambers [158][159][160], epicardial tissue [161], left atrial appendage [162,163], and coronary arteries [164]. Precise segmentation is invaluable as it facilitates quantification, classification, and visualization of medical image data, ultimately supporting more informed clinical decision-making processes.…”

Section: Image Segmentationmentioning

confidence: 99%

Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

Galić,

Habijan,

Leventić

et al. 2023

Electronics

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Artificial intelligence (AI) advancements, especially deep learning, have significantly improved medical image processing and analysis in various tasks such as disease detection, classification, and anatomical structure segmentation. This work overviews fundamental concepts, state-of-the-art models, and publicly available datasets in the field of medical imaging. First, we introduce the types of learning problems commonly employed in medical image processing and then proceed to present an overview of commonly used deep learning methods, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), with a focus on the image analysis task they are solving, including image classification, object detection/localization, segmentation, generation, and registration. Further, we highlight studies conducted in various application areas, encompassing neurology, brain imaging, retinal analysis, pulmonary imaging, digital pathology, breast imaging, cardiac imaging, bone analysis, abdominal imaging, and musculoskeletal imaging. The strengths and limitations of each method are carefully examined, and the paper identifies pertinent challenges that still require attention, such as the limited availability of annotated data, variability in medical images, and the interpretability issues. Finally, we discuss future research directions with a particular focus on developing explainable deep learning methods and integrating multi-modal data.

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Section: Image Segmentationmentioning

confidence: 99%

Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

Galić,

Habijan,

Leventić

et al. 2023

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [ 5 , 6 ] a related approach is presented using the polar transform as a pre-processing step. The main novelty in this paper is the use of cropping as a transformation step.…”

Section: Introductionmentioning

confidence: 99%

Segment-then-Segment: Context-Preserving Crop-Based Segmentation for Large Biomedical Images

Benčević

Qiu

Galić

et al. 2023

Sensors

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Medical images are often of huge size, which presents a challenge in terms of memory requirements when training machine learning models. Commonly, the images are downsampled to overcome this challenge, but this leads to a loss of information. We present a general approach for training semantic segmentation neural networks on much smaller input sizes called Segment-then-Segment. To reduce the input size, we use image crops instead of downscaling. One neural network performs the initial segmentation on a downscaled image. This segmentation is then used to take the most salient crops of the full-resolution image with the surrounding context. Each crop is segmented using a second specially trained neural network. The segmentation masks of each crop are joined to form the final output image. We evaluate our approach on multiple medical image modalities (microscopy, colonoscopy, and CT) and show that this approach greatly improves segmentation performance with small network input sizes when compared to baseline models trained on downscaled images, especially in terms of pixel-wise recall.

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Deep Learning-Based Segmentation and Mesh Reconstruction of the Aortic Vessel Tree from CTA Images

Vagenas,

Georgas,

Matsopoulos

2024

Lecture Notes in Computer Science

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Using the Polar Transform for Efficient Deep Learning-Based Aorta Segmentation in CTA Images

Cited by 4 publications

References 10 publications

Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

Segment-then-Segment: Context-Preserving Crop-Based Segmentation for Large Biomedical Images

Deep Learning-Based Segmentation and Mesh Reconstruction of the Aortic Vessel Tree from CTA Images

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