Details

Autor(en) / Beteiligte

• Lee, Jiyeon
• Lee, Minho
• Lee, Jongseung
• Kim, Regina E.Y.
• Lim, Seong Hoon
• Kim, Donghyeon

Titel

Fine-grained brain tissue segmentation for brain modeling of stroke patient

Ist Teil von

• Computers in biology and medicine, 2023-02, Vol.153, p.106472-106472, Article 106472

Ort / Verlag

United States: Elsevier Ltd

Erscheinungsjahr

2023

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Brain segmentation of stroke patients can facilitate brain modeling for electrical non-invasive brain stimulation, a therapy for stimulating brain function using an electric current. However, it remains challenging owing to its time-consuming, labor-dependent, and complicated pipeline. In addition, conventional tools that define lesions into one region rather than distinguishing between the stroke-affected regions and cerebrospinal fluid can lead to inaccurate treatment results. In this study, we first define a novel stroke-affected region as a detailed sub-region of the conventionally defined lesion. Subsequently, a novel comprehensive framework is proposed to segment head-brain and fine-level stroke-affected regions for normal controls and chronic stroke patients. The proposed framework consists of a time-efficient and precise deep learning-based segmentation model. The experiment results indicate that the proposed method perform better than the conventional deep learning-based segmentation model in terms of the evaluation metrics. The proposed method would be a valuable addition to brain modeling for non-invasive neuromodulation. •Fine-level lesions in T1 MR images are defined for brain modeling and segmentation.•A segmentation framework for fine-level stroke lesions and normal tissue is proposed.•The method shows more efficient performance than conventional deep learning-based models.•It is applicable for clinical non-invasive neuromodulation of stroke patients.