Keskeiset käsitteet
Incorporating both shape and topological priors into a unified latent representation improves the accuracy and anatomical consistency of automated liver vessel segmentation in medical images.
Tiivistelmä
This paper presents a novel approach called Joint Multi-Prior Encoding (JMPE) that integrates both shape and topological priors into a single latent space for improved liver vessel segmentation in medical images.
The key highlights are:
Vessel segmentation is a critical task in medical image analysis, but it is challenging due to the variability in vessel shape, size, and topology. Manual segmentation remains the gold standard, but is time-consuming and subjective.
Incorporating high-level anatomical priors, such as shape and topology, into deep learning-based segmentation models has been shown to improve accuracy by providing contextual information. However, previous approaches required training multiple encoders and tuning multiple hyperparameters.
The proposed JMPE method learns a single convolutional encoder that captures both shape and topological priors in a unified latent representation. This is achieved through a multi-task convolutional auto-encoder architecture that jointly reconstructs the segmentation mask and the Euclidean distance transform.
Experiments on the 3D-IRCADb dataset demonstrate that the JMPE-based segmentation model outperforms other approaches that incorporate either shape or topology priors individually, as well as a baseline 3D ResUNet model. The JMPE method shows improved performance in metrics like Dice similarity coefficient, Jaccard index, and connectivity-based measures.
The unified encoding scheme of JMPE reduces memory consumption compared to using separate encoders for shape and topology, while also simplifying the training process by requiring the tuning of a single hyperparameter.
Overall, the proposed JMPE approach holds promise in overcoming the challenges associated with automated vessel delineation and can potentially advance the field of deep priors encoding for medical image segmentation.
Tilastot
The Dice similarity coefficient (DSC) of the proposed JMPE method is 54.78%.
The Jaccard index (Jacc) of the proposed JMPE method is 38.00%.
The connectivity-based Dice similarity coefficient (clDSC) of the proposed JMPE method is 50.34%.
The Hausdorff distance (HD) of the proposed JMPE method is 67.06 mm.
The absolute volume difference (AVD) of the proposed JMPE method is 0.34 mm³.
The average symmetric surface distance (ASSD) of the proposed JMPE method is 4.77 mm.
Lainaukset
"Incorporating both shape and topology into the loss function requires training two separate encoders and setting two different hyper-parameters (Eq.8), which can be cumbersome and add complexity to the training process."
"The pursuit of learning multiple priors in a unified compact representation zzz, which we refer to as Joint Multi-Prior Encoding (JMPE), stands as a more efficient alternative than employing separate encodings zzzp."