μ-Net: A Deep Learning-Based Architecture for μ-CT Segmentation

• URL: http://arxiv.org/abs/2406.16724v1
• Date: Mon, 24 Jun 2024 15:29:08 GMT
• Title: μ-Net: A Deep Learning-Based Architecture for μ-CT Segmentation
• Authors: Pierangela Bruno, Edoardo De Rose, Carlo Adornetto, Francesco Calimeri, Sandro Donato, Raffaele Giuseppe Agostino, Daniela Amelio, Riccardo Barberi, Maria Carmela Cerra, Maria Caterina Crocco, Mariacristina Filice, Raffaele Filosa, Gianluigi Greco, Sandra Imbrogno, Vincenzo Formoso,
• Abstract summary: 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. extracting relevant information from 3D images requires semantic segmentation of the regions of interest. We propose a novel framework that uses a convolutional neural network (CNN) to automatically segment the full morphology of the heart of Carassius auratus.
• Score: 2.012378666405002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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