In recent years, continuous latent space (CLS) and discrete latent space (DLS) deep learning models have been proposed for medical image analysis for improved performance. However, these models encounter distinct challenges. CLS models capture intricate details but often lack interpretability in terms of structural representation and robustness due to their emphasis on low-level features. Conversely, DLS models offer interpretability, robustness, and the ability to capture coarse-grained information thanks to their structured latent space. However, DLS models have limited efficacy in capturing fine-grained details. To address the limitations of both DLS and CLS models, we propose SynergyNet, a novel bottleneck architecture designed to enhance existing encoder-decoder segmentation frameworks. SynergyNet seamlessly integrates discrete and continuous representations to harness complementary information and successfully preserves both fine and coarse-grained details in the learned representations. Our extensive experiment on multi-organ segmentation and cardiac datasets demonstrates that SynergyNet outperforms other state of the art methods, including TransUNet: dice scores improving by 2.16%, and Hausdorff scores improving by 11.13%, respectively. When evaluating skin lesion and brain tumor segmentation datasets, we observe a remarkable improvement of 1.71% in Intersection-over Union scores for skin lesion segmentation and of 8.58% for brain tumor segmentation. Our innovative approach paves the way for enhancing the overall performance and capabilities of deep learning models in the critical domain of medical image analysis.

翻译：近年来，连续潜在空间（CLS）与离散潜在空间（DLS）深度学习模型被相继提出以提升医学图像分析性能。然而，这些模型面临差异化挑战：CLS模型虽能捕捉精细细节，但因其对低层特征的侧重，在结构表示的可解释性与鲁棒性方面存在不足；相反，DLS模型凭借结构化潜在空间具有可解释性、鲁棒性及捕获粗粒度信息的能力，但在保留细粒度细节方面效能有限。为解决两类模型的局限性，我们提出SynergyNet——一种新颖的瓶颈架构，旨在增强现有编码器-解码器分割框架。SynergyNet无缝集成离散与连续表示，以利用互补信息，并成功在学习表示中同时保留细粒度与粗粒度细节。我们在多器官分割及心脏数据集上的广泛实验表明，SynergyNet超越包括TransUNet在内的现有最优方法：Dice分数提升2.16%，Hausdorff分数提升11.13%。在皮肤病变与脑肿瘤分割数据集的评估中，皮肤病变分割的交并比（Intersection-over Union）分数显著提升1.71%，脑肿瘤分割提升8.58%。本创新方法为在医学图像分析这一关键领域提升深度学习模型的整体性能与能力开辟了新路径。