X-ray computed microtomography ({\mu}-CT) is a non-destructive technique that
can generate high-resolution 3D images of the internal anatomy of medical and
biological samples. These images enable clinicians to examine internal anatomy
and gain insights into the disease or anatomical morphology. However,
extracting relevant information from 3D images requires semantic segmentation
of the regions of interest, which is usually done manually and results
time-consuming and tedious. In this work, we propose a novel framework that
uses a convolutional neural network (CNN) to automatically segment the full
morphology of the heart of Carassius auratus. The framework employs an
optimized 2D CNN architecture that can infer a 3D segmentation of the sample,
avoiding the high computational cost of a 3D CNN architecture. We tackle the
challenges of handling large and high-resoluted image data (over a thousand
pixels in each dimension) and a small training database (only three samples) by
proposing a standard protocol for data normalization and processing. Moreover,
we investigate how the noise, contrast, and spatial resolution of the sample
and the training of the architecture are affected by the reconstruction
technique, which depends on the number of input images. Experiments show that
our framework significantly reduces the time required to segment new samples,
allowing a faster microtomography analysis of the Carassius auratus heart
shape. Furthermore, our framework can work with any bio-image (biological and
medical) from {\mu}-CT with high-resolution and small dataset size

我们提出了一个使用卷积神经网络对鲫鱼心脏进行自动分割的新框架，该框架可以推断出样本的三维分割，从而避免了三维卷积神经网络的高计算成本，并且可以显著减少新样本分割所需的时间，实现对高分辨率鲫鱼心脏形状的快速微增强分析。