MRI segmentation in Magnetic Resonance Imaging is crucial for clinical diagnosis and treatment and recent deep learning methods have focused on refining network structures, they often neglect the inherent anatomical consistency of the prostate.
Abstract
Accurate prostate segmentation in Magnetic Resonance Imaging (MRI) is crucial for clinical diagnosis and treatment. However, it remains a challenging task due to low soft‐tissue contrast and structural ambiguity in the gland's appearance. While recent deep learning methods have focused on refining network structures, they often neglect the inherent anatomical consistency of the prostate.
Introduction Benefiting from its radiation-free property and excellent soft-tissue contrast, magnetic resonance imaging (MRI) has become a crucial modality for prostate cancer diagnosis. However, automatic segmentation of prostate cancer lesions in MRI is still a challenging task due to the considerable variations in l...
Bin Cai, Yun-Fu Zeng, Jiang Liu et al.· Frontiers in Physiology· 0 citations
The proposed model surpasses current methods and shows potential for practical clinical applications, providing an efficient, precise, and scalable solution for automated prostate cancer segmentation and fostering optimism for the future of medical imaging and diagnosis.
This study systematically evaluates how individual MRI sequences influence model robustness across two well-known datasets, and shows that the T2f/FLAIR sequence achieves the best cross-dataset performance, with Dice scores exceeding 75%.
Henrique Zan Grande, João G. Pitol, Lucas B. Schuck et al.· 0 citations
A lightweight Vision Transformer UNet is proposed that combines the hierarchical feature extraction capability of UNet with the global context modeling of Vision Transformers, enabling effective learning of both local and global features while maintaining computational efficiency with only 2.6 million trainable paramet...
Sheekar Banerjee, Monika Chowdhury, M. Akash et al.· 0 citations
Abstract This study proposes a 2.5D input framework for brain tumor segmentation in multi-sequence magnetic resonance imaging (MRI), aiming to balance computational efficiency and spatial context compared to conventional 2D approaches. While 2D methods are efficient but limited in capturing inter-slice information, the...
Sawehel Tlahig, Max Bindemann, T. Schanze· Current Directions in Biomed...· 0 citations
MMA-UNet, a novel hybrid architecture that integrates multiple complementary mechanisms within an encoder-decoder framework, demonstrates strong robustness on T1-weighted contrast-enhanced MRI, and its generalizability to larger datasets remains to be established.
