Volumetric biomedical microscopy has the potential to increase the diagnostic
information extracted from clinical tissue specimens and improve the diagnostic
accuracy of both human pathologists and computational pathology models.
Unfortunately, barriers to integrating 3-dimensional (3D) volumetric microscopy
into clinical medicine include long imaging times, poor depth / z-axis
resolution, and an insufficient amount of high-quality volumetric data.
Leveraging the abundance of high-resolution 2D microscopy data, we introduce
masked slice diffusion for super-resolution (MSDSR), which exploits the
inherent equivalence in the data-generating distribution across all spatial
dimensions of biological specimens. This intrinsic characteristic allows for
super-resolution models trained on high-resolution images from one plane (e.g.,
XY) to effectively generalize to others (XZ, YZ), overcoming the traditional
dependency on orientation. We focus on the application of MSDSR to stimulated
Raman histology (SRH), an optical imaging modality for biological specimen
analysis and intraoperative diagnosis, characterized by its rapid acquisition
of high-resolution 2D images but slow and costly optical z-sectioning. To
evaluate MSDSR's efficacy, we introduce a new performance metric, SliceFID, and
demonstrate MSDSR's superior performance over baseline models through extensive
evaluations. Our findings reveal that MSDSR not only significantly enhances the
quality and resolution of 3D volumetric data, but also addresses major
obstacles hindering the broader application of 3D volumetric microscopy in
clinical diagnostics and biomedical research.

通过利用高分辨率 2D 显微镜数据，我们引入了用于超分辨率的遮蔽切片扩散（MSDSR）方法，该方法克服了传统方法在不同方向上的依赖性，并增强了体积生物医学显微镜检测的质量、分辨率，以及应用于临床诊断和生物医学研究中的障碍。

具有 2D 监督的生物医学图像超分辨率

Super-resolution of biomedical volumes with 2D supervision

CNNs have been widely applied for medical image analysis. However, limited
memory capacity is one of the most common drawbacks of processing
high-resolution 3D volumetric data. 3D volumes are usually cropped or downsized
first before processing, which can result in a loss of resolution, increase
class imbalance, and affect the performance of the segmentation algorithms. In
this paper, we propose an end-to-end deep learning approach called IP-UNet.
IP-UNet is a UNet-based model that performs multi-class segmentation on
Intensity Projection (IP) of 3D volumetric data instead of the memory-consuming
3D volumes. IP-UNet uses limited memory capability for training without losing
the original 3D image resolution. We compare the performance of three models in
terms of segmentation accuracy and computational cost: 1) Slice-by-slice 2D
segmentation of the CT scan images using a conventional 2D UNet model. 2)
IP-UNet that operates on data obtained by merging the extracted Maximum
Intensity Projection (MIP), Closest Vessel Projection (CVP), and Average
Intensity Projection (AvgIP) representations of the source 3D volumes, then
applying the UNet model on the output IP images. 3) 3D-UNet model directly
reads the 3D volumes constructed from a series of CT scan images and outputs
the 3D volume of the predicted segmentation. We test the performance of these
methods on 3D volumetric images for automatic breast calcification detection.
Experimental results show that IP-Unet can achieve similar segmentation
accuracy with 3D-Unet but with much better performance. It reduces the training
time by 70\% and memory consumption by 92\%.

通过对 3D 体积数据进行强度投影分割，IP-UNet 模型在降低内存消耗的同时保持了原始 3D 图像分辨率，并且在乳房钙化检测中表现出类似的分割准确性，但具有更优异的性能。