DCSM 2.0: Deep Conditional Shape Models for Data Efficient Segmentation

TL;DR

DCSM 2.0 introduces a shape modeling approach that leverages cross-modality shape information and is highly data-efficient, outperforming traditional methods especially in low-data scenarios.

Contribution

It presents a novel deep conditional shape model that generalizes across domains and reduces data requirements for accurate segmentation.

Findings

Outperforms baseline in Hausdorff distances at all data levels.

Achieves up to 5% improvement in dice coefficient with minimal training data.

Scales effectively to low data regimes, maintaining high accuracy.

Abstract

Segmentation is often the first step in many medical image analyses workflows. Deep learning approaches, while giving state-of-the-art accuracies, are data intensive and do not scale well to low data regimes. We introduce Deep Conditional Shape Models 2.0, which uses an edge detector, along with an implicit shape function conditioned on edge maps, to leverage cross-modality shape information. The shape function is trained exclusively on a source domain (contrasted CT) and applied to the target domain of interest (3D echocardiography). We demonstrate data efficiency in the target domain by varying the amounts of training data used in the edge detection stage. We observe that DCSM 2.0 outperforms the baseline at all data levels in terms of Hausdorff distances, and while using 50% or less of the training data in terms of average mesh distance, and at 10% or less of the data with the dice coefficient. The method scales well to low data regimes, with gains of up to 5% in dice coefficient, 2.58 mm in average surface distance and 21.02 mm in Hausdorff distance when using just 2% (22 volumes) of the training data.