Cross-modality deep learning brings bright-field microscopy contrast to holography
Deep learning brings bright-field microscopy contrast to holographic images of a sample volume, bridging the volumetric imaging capability of holography with the speckle- and artifact-free image contrast of bright-field incoherent microscopy.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningSimilar Papers 제목 키워드 기반
High-Throughput Low-Cost Segmentation of Brightfield Microscopy Live Cell Images
Live cell culture is crucial in biomedical studies for analyzing cell properties and dynamics in vitro. This study focuses on segmenting unstained live cells imaged with bright-field microscopy. While many segmentation a…
Bright 4B: Scaling Hyperspherical Learning for Segmentation in 3D Brightfield Microscopy
Label-free 3D brightfield microscopy offers a fast and noninvasive way to visualize cellular morphology, yet robust volumetric segmentation still typically depends on fluorescence or heavy post-processing. We address thi…
Diffusion-Based Synthetic Brightfield Microscopy Images for Enhanced Single Cell Detection
Accurate single cell detection in brightfield microscopy is crucial for biological research, yet data scarcity and annotation bottlenecks limit the progress of deep learning methods. We investigate the use of uncondition…
Synthetic Data GenerationObject DetectionCell DetectionDeep-learning Assisted Detection and Quantification of (oo)cysts of Giardia and Cryptosporidium on Smartphone Microscopy Images
The consumption of microbial-contaminated food and water is responsible for the deaths of millions of people annually. Smartphone-based microscopy systems are portable, low-cost, and more accessible alternatives for the …
Deep Learningobject-detectionObject DetectionMulti-Modality Microscopy Image Style Transfer for Nuclei Segmentation
Annotating microscopy images for nuclei segmentation is laborious and time-consuming. To leverage the few existing annotations, also across multiple modalities, we propose a novel microscopy-style augmentation technique …
Data AugmentationGenerative Adversarial NetworkSegmentationStyle Transfer