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

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

TL;DR

The paper addresses semantic segmentation of high-resolution μ-CT images under very small datasets. It introduces μ-Net, a framework that decomposes the segmentation of heart anatomy into specialized 2D CNN-based models, followed by an ensemble stage to yield coherent 3D segmentations without resorting to computationally heavy 3D CNNs. The authors validate the approach on a newly collected goldfish heart μ-CT dataset with expert annotations and systematically study how projection dose and data normalization affect performance, showing robustness across resolutions. They report that μ-Net outperforms state-of-the-art methods (nnU-Net and Biomedisa) in IoU, speeds up processing, and generalizes to other high-resolution bio-imaging tasks with small datasets.

Abstract

X-ray computed microtomography (μ-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 μ-CT with high-resolution and small dataset size

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

TL;DR

The paper addresses semantic segmentation of high-resolution μ-CT images under very small datasets. It introduces μ-Net, a framework that decomposes the segmentation of heart anatomy into specialized 2D CNN-based models, followed by an ensemble stage to yield coherent 3D segmentations without resorting to computationally heavy 3D CNNs. The authors validate the approach on a newly collected goldfish heart μ-CT dataset with expert annotations and systematically study how projection dose and data normalization affect performance, showing robustness across resolutions. They report that μ-Net outperforms state-of-the-art methods (nnU-Net and Biomedisa) in IoU, speeds up processing, and generalizes to other high-resolution bio-imaging tasks with small datasets.

Abstract

X-ray computed microtomography (μ-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 μ-CT with high-resolution and small dataset size

Paper Structure

This paper contains 10 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Proposed approach
  3. Dataset building and description
  4. Experimental Design
  5. Data acquisition, 3D reconstruction and data preparation
  6. Training phase and evaluation metrics
  7. Ablation study
  8. Experiments
  9. Results and Discussion
  10. Conclusions

Figures (2)

  • Figure 1: Initially, the $\mu$-Net employs a CNN to identify the ventricles. Following this, two separate DL architectures carry out binary segmentation of various areas. The final result is obtained by applying an ensemble strategy to the different segmentations produced by each model.
  • Figure 2: Visualization of ground truth, $\mu$-Net and the comparison methods results. Each image represents a slice taken from the same quartile of slices within a single 3D stack