  Recent studies have demonstrated the superior performance of introducing
``scan-wise" contrast labels into contrastive learning for multi-organ
segmentation on multi-phase computed tomography (CT). However, such scan-wise
labels are limited: (1) a coarse classification, which could not capture the
fine-grained ``organ-wise" contrast variations across all organs; (2) the label
(i.e., contrast phase) is typically manually provided, which is error-prone and
may introduce manual biases of defining phases. In this paper, we propose a
novel data-driven contrastive loss function that adapts the similar/dissimilar
contrast relationship between samples in each minibatch at organ-level.
Specifically, as variable levels of contrast exist between organs, we
hypothesis that the contrast differences in the organ-level can bring
additional context for defining representations in the latent space. An
organ-wise contrast correlation matrix is computed with mean organ intensities
under one-hot attention maps. The goal of adapting the organ-driven correlation
matrix is to model variable levels of feature separability at different phases.
We evaluate our proposed approach on multi-organ segmentation with both
non-contrast CT (NCCT) datasets and the MICCAI 2015 BTCV Challenge
contrast-enhance CT (CECT) datasets. Compared to the state-of-the-art
approaches, our proposed contrastive loss yields a substantial and significant
improvement of 1.41% (from 0.923 to 0.936, p-value$<$0.01) and 2.02% (from
0.891 to 0.910, p-value$<$0.01) on mean Dice scores across all organs with
respect to NCCT and CECT cohorts. We further assess the trained model
performance with the MICCAI 2021 FLARE Challenge CECT datasets and achieve a
substantial improvement of mean Dice score from 0.927 to 0.934
(p-value$<$0.01). The code is available at: https://github.com/MASILab/DCC_CL
