Artificial Intelligence in Medical Image Processing and Segmentation

doi:10.3390/books978-3-0365-8586-4

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Hardback

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Keywords

fundus image; image registration; deep learning; computer vision applications; nuclei segmentation; histopathology; deep learning; Grad-CAM; semantic segmentation; instance segmentation; nuclei detection; pap smear; cervical net; shuffle net; canonical correlation analysis (CCA); support vector machine (SVM); random forest (RF); k-nearest neighbour (KNN); artificial neural network (ANN); dual-energy CT; two-step method; limited-angular range; directional total variation; PA; CNN; tooth disease recognition; image segmentation; image preprocessing; breast cancer; mitotic nuclei classification; histopathology images; artificial hummingbird algorithm; medical imaging; cervical cancer; feature fusion; feature selection; deep learning structures; support vector machine; disease discrimination accuracy; performance comparisons; 2D/3D registration; orthogonal X-ray; deep learning; breast density; CAD; image enhancement; breast cancer; deep learning; textural; auto-segmentation; deep learning; neuroimaging; magnetic resonance imaging; ovarian tumor; 2D ultrasound image; image inpainting; lesion segmentation; attention mechanism; GAN; deep learning; medical image analysis; synthetic CT; MRI guidance; MRI-only; image-guided radiotherapy; carbon ion radiotherapy; particle therapy; deep learning; rare tumor; PCNSL; radiomics; image normalization; MRI; prostate cancer; prostate segmentation; U-Net; mp-MRI; loss function; medical imaging; deep learning; semantic segmentation; automatic volume measurement; ultrasound bladder scanner; edge computing; urinary disease; artificial intelligence; mandible; segmentation; 3D virtual reconstruction; CBCT; CT; Convolutional Neural Networks; comparison; in-house; software; patch size; Cranio-Maxillofacial surgery; DICOM; osteoarthritis; histopathological; hematoxylin eosin; safranin O fast green; DarkNet-19; MobileNet; NasNet; ResNet-101; ShuffleNet; PCA; ALO; ensemble learning; OCT; pyramidal network; feature fusion; scale-adaptive; teeth segmentation; panoramic radiographs; mask-transformer-based networks; panoptic segmentation; tuberous sclerosis complex; children; convolutional neural network; multi-contrast MRI; rare neurodevelopmental disorder

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Artificial Intelligence in Medical Image Processing and Segmentation

Edited by

August 2023

348 pages

ISBN978-3-0365-8587-1 (Hardback)
ISBN978-3-0365-8586-4 (PDF)

https://doi.org/10.3390/books978-3-0365-8586-4

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This is a Reprint of the Special Issue Artificial Intelligence in Medical Image Processing and Segmentation that was published in

Biology & Life Sciences

Engineering

Summary

This reprint showcases a selection of bleeding-edge articles about medical image processing and segmentation workflows based on artificial intelligence algorithms. The proposed papers are applied to multiple and different anatomical districts and clinical scenarios.

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