- Title Information
- Title
- Development of a Deep Learning Framework for Assisting in Image-Guided Thermal Ablation of Kidney Tumors
- Type of Resource (primo)
- dissertations
- Name:
Personal
- Name Part
- Franco, Alvaro
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Kimia, Benjamin B.
- Role
- Role Term:
Text
- Advisor
- Name:
Personal
- Name Part
- Crisco, Joseph J.
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Jiao, Zhicheng
- Role
- Role Term:
Text
- Reader
- Name:
Corporate
- Name Part
- Brown University. Biology and Medicine: Biomedical Engineering
- Role
- Role Term:
Text
- sponsor
- Origin Information
- Copyright Date
- 2025
- Physical Description
- Extent
- 13, 77 p.
- digitalOrigin
- born digital
- Note:
thesis
- Thesis (Sc. M.)--Brown University, 2025
- Genre (aat)
- theses
- Abstract
- Renal cell carcinoma (RCC) is a life-threatening form of cancer causing tens of thousands of deaths in the USA yearly. Current treatment solutions mainly rely on partial or total nephrectomy, significantly reducing urinary function. Alternative treatments include ablation, a minimally invasive procedure that spares healthy tissues by directly applying energy to the tumor, reducing the toll on metabolic function. Ablation still remains underutilized due to the high recurrence suffered by patients. In this project, we study the viability of improving treatment procedures and outcomes of image-guided thermal ablation (IGTA) via deep learning algorithms. We make use of convolutional neural networks for the semantic segmentation of structures relevant to the disease (kidneys, tumors, and cysts), to automate the localization of the treatment zone. We make use of publicly available databases from the University of Minnesota, KiTS 19’ and KiTS 23’, and a private dataset provided by a team from Johns Hopkins University, to study the performance on pre- and intra-procedural image series. Our model demonstrates that it is a viable pipeline to assist in IGTA.
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00832568")
- Topic
- Biomedical engineering
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00817247")
- Topic
- Artificial intelligence
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/01045739")
- Topic
- Oncology
- Subject
- Topic
- Deep Learning
- Subject
- Topic
- Image Segmentation
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00892354")
- Topic
- Diagnostic imaging
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00987444")
- Topic
- Kidneys--Tumors
- Language
- Language Term (ISO639-2B)
- English
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20250707