  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
