1 citations · 2 across the 3 of their papers we have counts for
9 papers · 1 filter
From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstruction
Ayberk Acar, Mariana Smith, Lidia Al-Zogbi +14
Surgical automation requires precise guidance and understanding of the scene. Current methods in the literature rely on bulky depth cameras to create maps of the anatomy, however t…
Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images
Hao Li, Baris Oguz, Gabriel Arenas +6
Placenta volume measurement from 3D ultrasound images is critical for predicting pregnancy outcomes, and manual annotation is the gold standard. However, such manual annotation is…
PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts
Hao Li, Han Liu, Dewei Hu +2
In this paper, we present PRISM, a Promptable and Robust Interactive Segmentation Model, aiming for precise segmentation of 3D medical images. PRISM accepts various visual inputs,…
Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts
Hao Li, Han Liu, Dewei Hu +2
Interactive segmentation model leverages prompts from users to produce robust segmentation. This advancement is facilitated by prompt engineering, where interactive prompts serve a…
MAP: Domain Generalization via Meta-Learning on Anatomy-Consistent Pseudo-Modalities
Dewei Hu, Hao Li, Han Liu +3
Deep models suffer from limited generalization capability to unseen domains, which has severely hindered their clinical applicability. Specifically for the retinal vessel segmentat…
False Negative/Positive Control for SAM on Noisy Medical Images
Xing Yao, Han Liu, Dewei Hu +9
The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. It can use sparse manual prompts such as bounding boxes to generate pixe…