Despite their effectiveness, current deep learning models face challenges
with images coming from different domains with varying appearance and content.
We introduce SegCLR, a versatile framework designed to segment volumetric
images across different domains, employing supervised and contrastive learning
simultaneously to effectively learn from both labeled and unlabeled data. We
demonstrate the superior performance of SegCLR through a comprehensive
evaluation involving three diverse clinical datasets of retinal fluid
segmentation in 3D Optical Coherence Tomography (OCT), various network
configurations, and verification across 10 different network initializations.
In an unsupervised domain adaptation context, SegCLR achieves results on par
with a supervised upper-bound model trained on the intended target domain.
Notably, we discover that the segmentation performance of SegCLR framework is
marginally impacted by the abundance of unlabeled data from the target domain,
thereby we also propose an effective zero-shot domain adaptation extension of
SegCLR, eliminating the need for any target domain information. This shows that
our proposed addition of contrastive loss in standard supervised training for
segmentation leads to superior models, inherently more generalizable to both
in- and out-of-domain test data. We additionally propose a pragmatic solution
for SegCLR deployment in realistic scenarios with multiple domains containing
labeled data. Accordingly, our framework pushes the boundaries of deep-learning
based segmentation in multi-domain applications, regardless of data
availability - labeled, unlabeled, or nonexistent.

SegCLR 是一个多功能的框架，旨在通过同时采用监督学习和对比学习，跨不同领域对体积图像进行分割，有效地从标记和无标记数据中学习。

联合半监督和对比学习实现零样本域自适应与多域分割

Joint semi-supervised and contrastive learning enables zero-shot  domain-adaptation and multi-domain segmentation

Deep learning-based lossless compression methods offer substantial advantages
in compressing medical volumetric images. Nevertheless, many learning-based
algorithms encounter a trade-off between practicality and compression
performance. This paper introduces a hardware-friendly streaming lossless
volumetric compression framework, utilizing merely one-thousandth of the model
weights compared to other learning-based compression frameworks. We propose a
gated recurrent convolutional neural network that combines diverse
convolutional structures and fusion gate mechanisms to capture the inter-slice
dependencies in volumetric images. Based on such contextual information, we can
predict the pixel-by-pixel distribution for entropy coding. Guided by
hardware/software co-design principles, we implement the proposed framework on
Field Programmable Gate Array to achieve enhanced real-time performance.
Extensive experimental results indicate that our method outperforms traditional
lossless volumetric compressors and state-of-the-art learning-based lossless
compression methods across various medical image benchmarks. Additionally, our
method exhibits robust generalization ability and competitive compression speed

该论文介绍了一种基于深度学习的无损压缩方法，针对医学体积图像提出了一个只需要其他学习型压缩方法的千分之一的模型权重的硬件友好流式无损体积压缩框架，通过融合不同的卷积结构和门控循环卷积神经网络机制来捕捉切面间的依赖关系，并基于这样的上下文信息来预测像素级的熵编码分布，通过硬件 / 软件的协同设计原则，在可编程门阵列上实现了提出的框架以实现增强的实时性能，在各种医学图像基准测试中，广泛的实验结果显示我们的方法优于传统无损体积压缩器和现有的学习型无损压缩方法，同时我们的方法表现出强大的泛化能力和有竞争力的压缩速度。

使用门控循环卷积神经网络对医学图像进行流式无损体积压缩

Streaming Lossless Volumetric Compression of Medical Images Using Gated  Recurrent Convolutional Neural Network

Recent advances in 3D fully convolutional networks (FCN) have made it
feasible to produce dense voxel-wise predictions of volumetric images. In this
work, we show that a multi-class 3D FCN trained on manually labeled CT scans of
several anatomical structures (ranging from the large organs to thin vessels)
can achieve competitive segmentation results, while avoiding the need for
handcrafting features or training class-specific models.
To this end, we propose a two-stage, coarse-to-fine approach that will first
use a 3D FCN to roughly define a candidate region, which will then be used as
input to a second 3D FCN. This reduces the number of voxels the second FCN has
to classify to ~10% and allows it to focus on more detailed segmentation of the
organs and vessels.
We utilize training and validation sets consisting of 331 clinical CT images
and test our models on a completely unseen data collection acquired at a
different hospital that includes 150 CT scans, targeting three anatomical
organs (liver, spleen, and pancreas). In challenging organs such as the
pancreas, our cascaded approach improves the mean Dice score from 68.5 to
82.2%, achieving the highest reported average score on this dataset. We compare
with a 2D FCN method on a separate dataset of 240 CT scans with 18 classes and
achieve a significantly higher performance in small organs and vessels.
Furthermore, we explore fine-tuning our models to different datasets.
Our experiments illustrate the promise and robustness of current 3D FCN based
semantic segmentation of medical images, achieving state-of-the-art results.
Our code and trained models are available for download:
this https URL

通过在多个解剖结构 (从大器官到细小血管) 的计算机断层扫描手动标记的数据上训练多分类的三维卷积神经网络，该研究展示了基于三维全卷积神经网络的医学图像语义分割的实现和鲁棒性的突破，所提出的粗略到精细的两阶段方法达到了最先进水平。