AI RESEARCH
ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation
arXiv CS.CV
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ArXi:2604.24876v1 Announce Type: new Text guided 3D medical image segmentation offers a flexible alternative to class based and spatial prompt based models by allowing users to specify regions of interest directly in natural language. This paradigm avoids reliance on predefined label sets, reduces ambiguous outputs, and aligns naturally with clinical workflows. However, existing text guided frameworks are often computationally expensive, exhibit weak text volume feature alignment, and fail to capture fine anatomical details.