SALT: Introducing a Framework for Hierarchical Segmentations in Medical Imaging using Softmax for Arbitrary Label Trees
arxiv(2024)
摘要
Traditional segmentation networks approach anatomical structures as
standalone elements, overlooking the intrinsic hierarchical connections among
them. This study introduces Softmax for Arbitrary Label Trees (SALT), a novel
approach designed to leverage the hierarchical relationships between labels,
improving the efficiency and interpretability of the segmentations.
This study introduces a novel segmentation technique for CT imaging, which
leverages conditional probabilities to map the hierarchical structure of
anatomical landmarks, such as the spine's division into lumbar, thoracic, and
cervical regions and further into individual vertebrae. The model was developed
using the SAROS dataset from The Cancer Imaging Archive (TCIA), comprising 900
body region segmentations from 883 patients. The dataset was further enhanced
by generating additional segmentations with the TotalSegmentator, for a total
of 113 labels. The model was trained on 600 scans, while validation and testing
were conducted on 150 CT scans. Performance was assessed using the Dice score
across various datasets, including SAROS, CT-ORG, FLARE22, LCTSC, LUNA16, and
WORD.
Among the evaluated datasets, SALT achieved its best results on the LUNA16
and SAROS datasets, with Dice scores of 0.93 and 0.929 respectively. The model
demonstrated reliable accuracy across other datasets, scoring 0.891 on CT-ORG
and 0.849 on FLARE22. The LCTSC dataset showed a score of 0.908 and the WORD
dataset also showed good performance with a score of 0.844.
SALT used the hierarchical structures inherent in the human body to achieve
whole-body segmentations with an average of 35 seconds for 100 slices. This
rapid processing underscores its potential for integration into clinical
workflows, facilitating the automatic and efficient computation of full-body
segmentations with each CT scan, thus enhancing diagnostic processes and
patient care.
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