Recent advancements in biomedical image analysis have been significantly driven by the
Segment Anything Model (SAM). This transformative technology, originally developed for …

Interactive segmentation model leverages prompts from users to produce robust
segmentation. This advancement is facilitated by prompt engineering, where interactive …

Medical imaging segmentation has been a prominent focus in the field of medical imaging
analysis. Recent advances in radiological and storage technologies have led to an …

The Medical Segment Anything Model (MedSAM) has shown remarkable performance in
medical image segmentation, drawing significant attention in the field. However, its …

Breast ultrasound image segmentation is a challenging task due to the low contrast and
blurred boundary between the breast mass and the background. Our goal is to utilize the …

The accurate reconstruction of surgical scenes from surgical videos is critical for various
applications, including intraoperative navigation and image-guided robotic surgery …

Placenta volume measurement from 3D ultrasound images is critical for predicting
pregnancy outcomes, and manual annotation is the gold standard. However, such manual …

The Segment Anything Model (SAM) has recently emerged as a groundbreaking foundation
model for prompt-driven image segmentation tasks. However, both the original SAM and its …

Convolutional Neural Networks (CNNs) exhibit strong performance in medical image
segmentation tasks by capturing high-level (local) information, such as edges and textures …

Convolutional Neural Networks (CNNs) have exhibited strong performance in medical
image segmentation tasks by capturing high-level (local) information, such as edges and …