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_aLow Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance _h[electronic resource] : _bSecond MICCAI Challenge, LISA 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025, Proceedings / _cedited by Natasha Lepore, Marius George Linguraru. |
| 250 | _a1st ed. 2026. | ||
| 264 | 1 |
_aCham : _bSpringer Nature Switzerland : _bImprint: Springer, _c2026. |
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_aX, 133 p. 34 illus., 28 illus. in color. _bonline resource. |
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_bSingle logical reading order _2onix |
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_aLecture Notes in Computer Science, _x1611-3349 ; _v16411 |
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| 505 | 0 | _a -- Task 1 - Automatic Ultra-Low Field MR Image Quality Assessment -- BRIQA: Balanced Reweighting in Image Quality Assessment of Pediatric Brain MRI. -- Robust Multi-Label Classification of MRI Artifacts in Low-Field Neonatal Brain Imaging via View-Conditional Dual Task Learning. -- Task 2b - Automatic Basal Ganglia Segmentation from Ultra-Low Field MRI -- Towards Robust Basal Ganglia Segmentation in Ultra-Low-Field Pediatric MRI via an Optimized MS-TCNet. -- Tasks 2a and 2b - Automatic Hippocampal and Basal Ganglia Segmentation form Ultra-Low Field MRI -- Segmenting Brain Regions in Low Field Pediatric Brain MR Images using (symmetric) nnU-Net ResEnc. -- Segmenting infant brains across magnetic fields: Domain randomization and annotation curation in ultra-low field MRI. -- Enforcing Anatomical Symmetry with Euclidean Distance Transforms for Low-Field MRI Bilateral Structure Segmentation. -- Coordinate Transformations Make Segmentation Models More Data-Efficient. -- Atlas-Augmented Semantic Segmentation for Robust Ultra-Low-Field Pediatric Brain Imaging. -- Automated Pediatric Brain Hippocampal and Basal Ganglia Segmentation in Ultra-Low Field Magnetic Resonance Images. -- Tasks 1, 2a and 2b Combined -- Application of Vision Transformers to Multi-Task Learning in the LISA 2025 MRI Challenge. -- Automatic Quality Assurance and Subcortical Brain Segmentation in Pediatric Ultra-Low-Field MRI: Exploring Ordinal Learning and Foundation Model Adaptation. | |
| 506 | 0 | _aOpen Access | |
| 520 | _aThis open access book constitutes the proceedings of the LISA 2025 Challenge, held in conjunction with MICCAI 2025 in Daejeon, South Korea, during September 27, 2025. The LISA Challenge serves as a benchmarking platform for developing evaluating automatic image analysis and machine learning algorithms. The 11 full papers included in the book were carefully reviewed and selected from 14 final submissions.The papers are organized in topical sections as follows: Task 1 - Automatic Ultra-Low Field MR Image Quality Assessment; Task 2b - Automatic Basal Ganglia Segmentation from Ultra-Low Field MRI; Tasks 2a and 2b - Automatic Hippocampal and Basal Ganglia Segmentation form Ultra-Low Field MRI; and Tasks 1, 2a and 2b Combined. . | ||
| 532 | 8 | _aAccessibility summary: This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. Please note that a more accessible version of this eBook is available as ePub. | |
| 532 | 8 | _aNo reading system accessibility options actively disabled | |
| 532 | 8 | _aPublisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 650 | 0 |
_aImage processing _xDigital techniques. |
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| 650 | 0 | _aComputer vision. | |
| 650 | 0 | _aComputational neuroscience. | |
| 650 | 0 |
_aNuclear magnetic resonance. _923615 |
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| 650 | 1 | 4 | _aComputer Imaging, Vision, Pattern Recognition and Graphics. |
| 650 | 2 | 4 | _aComputational Neuroscience. |
| 650 | 2 | 4 |
_aMagnetic Resonance (NMR, EPR). _923616 |
| 700 | 1 |
_aLepore, Natasha. _eeditor. _0(orcid)0000-0002-8379-674X _1https://orcid.org/0000-0002-8379-674X _4edt _4http://id.loc.gov/vocabulary/relators/edt _919067 |
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| 700 | 1 |
_aLinguraru, Marius George. _eeditor. _0(orcid)0000-0001-6175-8665 _1https://orcid.org/0000-0001-6175-8665 _4edt _4http://id.loc.gov/vocabulary/relators/edt _919068 |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783032144164 |
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_iPrinted edition: _z9783032144188 |
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_aLecture Notes in Computer Science, _x1611-3349 ; _v16411 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-032-14417-1 |
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