Handbook of Medical Image Computing and Computer Assisted Intervention
Chapter 18 - Radiomics: Data mining using quantitative medical image features
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The effect of morphometric and geometric indices of the human calvarium on mechanical response
2023, Clinical BiomechanicsModality-level cross-connection and attentional feature fusion based deep neural network for multi-modal brain tumor segmentation
2023, Biomedical Signal Processing and ControlCitation Excerpt :Manual brain tumor segmentation requires a lot of expertise, and the segmentation is often achieved in a slice-by-slice manner. Although we can take advantage of the expert knowledge, this method is very tedious and time-consuming, and the results are very subjective [4,5]. In the last few years, a large number of automated approaches have been proposed to segment the brain tumors, which can be generally categorized into two groups:
Deep Network Design for Medical Image Computing: Principles and Applications
2023, Deep Network Design for Medical Image Computing: Principles and ApplicationsOpenFiberSeg: Open-source segmentation of individual fibers and porosity in tomographic scans of additively manufactured short fiber reinforced composites
2022, Composites Science and TechnologyCitation Excerpt :As imaging apparatus are limited to a voxel size of 0.7 to 0.4 μm, fiber identification must be performed on at best a handful of voxels, as they typically have a diameter ranging from 5 to 10 μm. Manual labelling by an expert is possible, but is a tedious, time-consuming task, and subject to inter and intra-observer variability [37]. Automatic segmentation tools are therefore required.
Toward High-Throughput Artificial Intelligence-Based Segmentation in Oncological PET Imaging
2021, PET ClinicsCitation Excerpt :Accurate segmentation is also crucial for external beam therapy planning. In the last decade, the valuable role of radiomics4,5 for image assessment and outcome prediction has been reported, for which segmentation is a vital step.6–9 In clinical workflows, in the context of radiopharmaceutical therapies, segmentation of PET and/or single-photon emission computed tomographic (SPECT) images is also needed for image-based dosimetry as well as quantification of therapy response based on pretherapeutic and posttherapeutic images (see Julia Brosch-Lenz and colleagues’ article, “Role of AI in Theranostics: Towards Routine Personalized Radiopharmaceutical Therapies,” in this issue).
Role of Artificial Intelligence in Theranostics:: Toward Routine Personalized Radiopharmaceutical Therapies
2021, PET ClinicsCitation Excerpt :Segmentation of all these lesions manually is not practical. Manual segmentation is also subject to intraobserver53 and interobserver variability.54 Validated AI-based models for fully automated, robust, accurate segmentation of organs/lesions in PET, PET/CT, and SPECT/CT images can help delineate OARs and lesions to achieve a personalized dosimetry framework.
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The first two authors are shared first authors.