MKNet-family architectures for auto-segmentation of the residual pancreas after pancreatic resection: a deep learning comparative study.
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| Title: | MKNet-family architectures for auto-segmentation of the residual pancreas after pancreatic resection: a deep learning comparative study. |
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| Authors: | Böhm D; Datacation B.V., Eindhoven, The Netherlands.; Delft University of Technology, TU Delft, The Netherlands., Andel PCM; Regional Academic Cancer Center Utrecht, UMC Utrecht Cancer Center & St. Antonius Hospital Nieuwegein, Department of Surgery, Utrecht, The Netherlands., Akkermans PA; Medical Spectrum Twente, Department of Radiology, Enschede, The Netherlands., Boekestijn B; Leiden University Medical Center, Department of Radiology, Leiden, The Netherlands., van der Geest W; Datacation B.V., Eindhoven, The Netherlands., de Haas RJ; University Medical Center Groningen, Department of Radiology, Groningen, The Netherlands., Kist JW; Amsterdam UMC, location University of Amsterdam, Department of Radiology, Amsterdam, The Netherlands., Molenaar IQ; Regional Academic Cancer Center Utrecht, UMC Utrecht Cancer Center & St. Antonius Hospital Nieuwegein, Department of Surgery, Utrecht, The Netherlands., Nederend J; Catharina Hospital Eindhoven, Department of Radiology, Eindhoven, The Netherlands., Nio CY; Amsterdam UMC, location University of Amsterdam, Department of Radiology, Amsterdam, The Netherlands., Pranger BK; UMC Utrecht Cancer Center, Department of Radiology, Utrecht, The Netherlands., van Santvoort HC; Regional Academic Cancer Center Utrecht, UMC Utrecht Cancer Center & St. Antonius Hospital Nieuwegein, Department of Surgery, Utrecht, The Netherlands., Struik F; Amsterdam UMC, location University of Amsterdam, Department of Radiology, Amsterdam, The Netherlands., Verpalen IM; Amsterdam UMC, location University of Amsterdam, Department of Radiology, Amsterdam, The Netherlands., Wessels FJ; UMC Utrecht Cancer Center, Department of Radiology, Utrecht, The Netherlands., Veldhuis WB; UMC Utrecht Cancer Center, Department of Radiology, Utrecht, The Netherlands., Verkooijen HM; University Medical Center Utrecht, Division of Imaging and Oncology, Utrecht,, The Netherlands., Willemssen FEJA; Erasmus MC, University Medical Center Rotterdam, Department of Radiology and Nuclear Medicine, Delft, The Netherlands., Zoetekouw RI; Datacation B.V., Eindhoven, The Netherlands., Dijkstra J; Leiden University Medical Center, Department of Radiology, Leiden, The Netherlands., Intven MPW; UMC Utrecht Cancer Center, Department of Radiation Oncology, Utrecht, The Netherlands., Weinmann M; Delft University of Technology, TU Delft, The Netherlands., Daamen LA; Regional Academic Cancer Center Utrecht, UMC Utrecht Cancer Center & St. Antonius Hospital Nieuwegein, Department of Surgery, Utrecht, The Netherlands. l.a.daamen-3@umcutrecht.nl.; University Medical Center Utrecht, Division of Imaging and Oncology, Utrecht,, The Netherlands. l.a.daamen-3@umcutrecht.nl. |
| Source: | Abdominal radiology (New York) [Abdom Radiol (NY)] 2026 Jul; Vol. 51 (7), pp. 3492-3503. Date of Electronic Publication: 2025 Nov 27. |
| Publication Type: | Journal Article; Comparative Study |
| Journal Info: | Publisher: Springer Country of Publication: United States NLM ID: 101674571 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2366-0058 (Electronic) NLM ISO Abbreviation: Abdom Radiol (NY) Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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