DEPT: Deep Extreme Point Tracing for Ultrasound Image Segmentation
This work addresses the labor-intensive annotation challenge in medical imaging for clinicians and researchers, but it is incremental as it builds on existing weakly supervised approaches.
The paper tackles the problem of reducing annotation effort in medical image segmentation by proposing DEPT with FGEPM, a weakly supervised method using extreme points, which approaches fully supervised performance and outperforms other weakly supervised methods on two public ultrasound datasets.
Automatic medical image segmentation plays a crucial role in computer aided diagnosis. However, fully supervised learning approaches often require extensive and labor-intensive annotation efforts. To address this challenge, weakly supervised learning methods, particularly those using extreme points as supervisory signals, have the potential to offer an effective solution. In this paper, we introduce Deep Extreme Point Tracing (DEPT) integrated with Feature-Guided Extreme Point Masking (FGEPM) algorithm for ultrasound image segmentation. Notably, our method generates pseudo labels by identifying the lowest-cost path that connects all extreme points on the feature map-based cost matrix. Additionally, an iterative training strategy is proposed to refine pseudo labels progressively, enabling continuous network improvement. Experimental results on two public datasets demonstrate the effectiveness of our proposed method. The performance of our method approaches that of the fully supervised method and outperforms several existing weakly supervised methods.