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Suitability of DNN-based vessel segmentation for SIRT planning.
Kock, Farina; Thielke, Felix; Abolmaali, Nasreddin; Meine, Hans; Schenk, Andrea.
Affiliation
  • Kock F; Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Str. 2, Bremen, 28359, Germany. farina.kock@mevis.fraunhofer.de.
  • Thielke F; Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Str. 2, Bremen, 28359, Germany.
  • Abolmaali N; Diagnostic and Interventional Radiology and Nuclear Medicine, St. Josef-Hospital, University Hospitals of the Ruhr University of Bochum, Gudrunstr. 56, Bochum, 44791, Germany.
  • Meine H; Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Str. 2, Bremen, 28359, Germany.
  • Schenk A; Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Str. 2, Bremen, 28359, Germany.
Int J Comput Assist Radiol Surg ; 19(2): 233-240, 2024 Feb.
Article in En | MEDLINE | ID: mdl-37535263
ABSTRACT

PURPOSE:

The segmentation of the hepatic arteries (HA) is essential for state-of-the-art pre-interventional planning of selective internal radiation therapy (SIRT), a treatment option for malignant tumors in the liver. In SIRT a catheter is placed through the aorta into the tumor-feeding hepatic arteries, injecting small beads filled with radiation emitting material for local radioembolization. In this study, we evaluate the suitability of a deep neural network (DNN) based vessel segmentation for SIRT planning.

METHODS:

We applied our DNN-based HA segmentation on 36 contrast-enhanced computed tomography (CT) scans from the arterial contrast agent phase and rated its segmentation quality as well as the overall image quality. Additionally, we applied a traditional machine learning algorithm for HA segmentation as comparison to our deep learning (DL) approach. Moreover, we assessed by expert ratings whether the produced HA segmentations can be used for SIRT planning.

RESULTS:

The DL approach outperformed the traditional machine learning algorithm. The DL segmentation can be used for SIRT planning in [Formula see text] of the cases, while the reference segmentations, which were manually created by experienced radiographers, are sufficient in [Formula see text]. Seven DL cases cannot be used for SIRT planning while the corresponding reference segmentations are sufficient. However, there are two DL segmentations usable for SIRT, where the reference segmentations for the same cases were rated as insufficient.

CONCLUSIONS:

HA segmentation is a difficult and time-consuming task. DL-based methods have the potential to support and accelerate the pre-interventional planning of SIRT therapy.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Neural Networks, Computer / Liver Neoplasms Limits: Humans Language: En Journal: Int J Comput Assist Radiol Surg Journal subject: RADIOLOGIA Year: 2024 Document type: Article Affiliation country: Alemania

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Neural Networks, Computer / Liver Neoplasms Limits: Humans Language: En Journal: Int J Comput Assist Radiol Surg Journal subject: RADIOLOGIA Year: 2024 Document type: Article Affiliation country: Alemania