Boundary guidance network for medical image segmentation

• Published in: Scientific reports Vol. 14; no. 1; pp. 17345 - 14
• Main Authors: Xu, Rubin, Xu, Chao, Li, Zhengping, Zheng, Tianyu, Yu, Weidong, Yang, Cheng
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 28.07.2024; Nature Publishing Group UK; Nature Portfolio
• Subjects: Algorithms; Bladder; Bladder cancer; Cystoscopy - methods; Deep Learning; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Neural Networks, Computer; Tomography, X-Ray Computed - methods; Tumors; Urinary Bladder Neoplasms - diagnostic imaging; Urinary Bladder Neoplasms - pathology
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-67554-0

Accurate segmentation of the tumor area is crucial for the treatment and prognosis of patients with bladder cancer. Cystoscopy is the gold standard for diagnosing bladder tumors. However, The vast majority of current work uses deep learning to identify and segment tumors from CT and MRI findings, and rarely involves cystoscopy findings. Accurately segmenting bladder tumors remains a great challenge due to their diverse morphology and fuzzy boundaries. In order to solve the above problems, this paper proposes a medical image segmentation network with boundary guidance called boundary guidance network. This network combines local features extracted by CNNs and long-range dependencies between different levels inscribed by Parallel ViT, which can capture tumor features more effectively. The Boundary extracted module is designed to extract boundary features and utilize the boundary features to guide the decoding process. Foreground-background dual-channel decoding is performed by boundary integrated module. Experimental results on our proposed new cystoscopic bladder tumor dataset (BTD) show that our method can efficiently perform accurate segmentation of tumors and retain more boundary information, achieving an IoU score of 91.3%, a Hausdorff Distance of 10.43, an mAP score of 85.3%, and a F1 score of 94.8%. On BTD and three other public datasets, our model achieves the best scores compared to state-of-the-art methods, which proves the effectiveness of our model for common medical image segmentation.