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State University of New York at Buffalo

A Comparative Study of Novel Deep Learning-Based and Conventional Atlas-Based Automatic Segmentation in Head and Neck Radiotherapy Planning

dc:creator Asbach, John; 0000-0002-7300-5967

dc:date2025

Abstract

dc:description

M.S.

Degree

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Grantor dc:publisher: State University of New York at Buffalo
Year dc:date: 2025

Author and committee

dc:creator, dc:contributor.*

Author dc:creator: Asbach, John; 0000-0002-7300-5967
Contributors dc:contributor: Le, Anh
Radiology

Subjects

dc:subject × 4

Rights

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Statement dc:rights: Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.

Copyright retained by author.
Language dc:language: eng

Identifiers

dc:identifier.*

Handle dc:identifier: http://hdl.handle.net/10477/86680
OAI identifier oai:identifier: oai:ubir.buffalo.edu:10477/86680

Chain of custody

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Harvested from: Buffalo
Base URL: ubir.buffalo.edu/oai/request
Last updated: 2026-07-27
Source record: OAI-PMH GetRecord

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citation

Asbach, John; 0000-0002-7300-5967. A Comparative Study of Novel Deep Learning-Based and Conventional Atlas-Based Automatic Segmentation in Head and Neck Radiotherapy Planning. State University of New York at Buffalo, 2025. http://hdl.handle.net/10477/86680

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