Accelerate your imaging data processing, segmentation, and classification using Machine and Deep Learning in Amira, Avizo, and PerGeos Software

Artificial intelligence (AI) methods, such as machine learning and deep learning, have proven to be powerful approaches for automating image segmentation and improving image quality.

The use of AI-based tools in Thermo Scientific Amira-Avizo 2D Software, Amira-Avizo Software, and PerGeos Software is a major leap forward and enriches processing capabilities by allowing the ability to mix both traditional and AI-based algorithms.

Deep Learning in Amira-Avizo Software and PerGeos Software

Deep-learned neural networks have proven to be invaluable tools for many research and industrial purposes in recent years. Using deep learning for processing images allows researchers to go beyond traditional image processing for greatly improved results.

Amira-Avizo Software and PerGeos Software provide ideal environments for deep learning.

A rich image pre/post-processing toolbox supplementing user-friendly manual and supervised segmentation allows enhanced data annotation and preparation for the training phase and the prediction phase. It also leverages the actual model building, training, and prediction steps from experienced deep learning frameworks, such as TensorFlow and Keras. The workflow for learning from a manually segmented subset and performing the prediction on a complete data set is as simple as applying two modules.

Deep learning model trained on a subvolume for detecting membranes and mitochondria, prediction applied on the full volume. Data courtesy of Cardona A, et al. 2010. An Integrated Micro- and Macroarchitectural Analysis of the Drosophila Brain by Computer-Assisted Serial Section Electron Microscopy. PLoS Biol 8(10): e1000502. doi:10.1371/journal.pbio.1000502.

Deep Learning off-the-shelf

The Deep Learning Prediction module allows for trained models to be used on data. It can be incorporated within any workflow or recipe to automate your image processing, segmentation, or analysis tasks.


The deep learning training modules feature a highly configurable tool for training models using state-of-the-art architectures, such as Unet with Resnet or VGG backbones, data augmentation, a selection of loss, and metric functions. The training can occur from scratch (random weights) or from pre-trained weights.


The training is monitored in real time using TensorBoard to track metrics, such as loss and accuracy, or to visualize the model’s architecture.

Watch accuracy and loss on training and validation sets.

Customizable solution

Amira-Avizo Software and PerGeos Software’s latest versions provide a default set of deep learning Python packages and modules based on Keras, a high-level neural networks API running on top of TensorFlow. You can go further by customizing your Python environment or creating multiple self-contained Python environments with their own sets of packages and use them within your own Python Script Modules (pyscro).

Advanced users and Python programmers can customize both the Deep Learning Training and Prediction modules. A plugin system allows for the definition of custom model architectures, loss, or metric functions, which can be readily available from the GUI Deep Learning Training module.

Prediction can also be customized from pre- to post-prediction processing, to enable full control on the input and outputs and to optimize memory usage.

Applications and use cases

Image segmentation of mitochondria blobs

Mitochondria are difficult to segment using traditional approaches because they have connections with the outer endoplasmic reticulum and an internal membrane-like structure.

The model trained with Amira-Avizo Software’s deep learning tool allows the automatic extraction of mitochondria from a FIB-SEM stack. The training was done using only a few slices, which were segmented manually with Amira-Avizo Software’s segmentation editor. It was then possible to automatically segment the rest of the stack, saving hours of manual work.


(Left) Manual segmentation using Amira-Avizo Software's segmentation editor, and (right) 3D visualization of the mitochondria from the automatic segmentation of the full stack with deep learning. Data courtesy of Advanced Imaging Res. Center, Kurume Univ. Sch. Med.

Image denoising of SEM

For 3D serial sectioning and 2D tiling applications, time to data versus image quality must be carefully balanced. Usually, the data is heavily down-sampled to process it. Following acquisition, conventional algorithms, such as gaussian-smoothing and non-local-means filtering, leave artifacts. Alternatively, deep learning algorithms can be tuned in such a way that they do not induce artifacts. Processing can be done relatively quickly when a deep learning model is available. Below, we highlight a model that can quickly restore SEM images.

Image super-resolution

High-resolution images are often needed to clearly capture desired structural details, while lower resolution acquisition may be imposed by exposure time and dose applied to the sample.
Super-resolution deep learning algorithms can restore realistic details from lower resolution images, dramatically facilitating image segmentation.


Deep Learning segmentation of pores in solid oxide fuel cells in FIB-SEM

FIB-SEM 3D images of non-impregnated porous media suffered from a so-called pore-back effect, in which the back of a pore can be easily confused with a solid material lying on the section. In this case, we trained a deep learning model to recognize pore-backs by segmenting a number of image patches using traditional supervised techniques.

Illustration of the training of a deep learning model based on patches, including prediction on the full dataset. Solid Oxide Fuel Cell dataset courtesy of Sabanci University, Turkey.

Facies Classification with Supervised Machine Learning

Rock type (facies) identification plays a key role in the exploration and development of oil and gas reservoirs. Traditional core-based facies identification is costly, time consuming, and subjective. Machine Learning allows you to address the challenges in a fully automated and reproducible way.


Color Auto Classification based on machine learning

Based on machine learning, the Color Auto Classification tool automatically segments a color image into labels. A supervised random forest method is used.

Automatic segmentation of an optical image of a thin section using Color Auto Classification. Data courtesy of Stratum Reservoir.

Texture Supervised Classification based on machine learning

Texture classification is a machine learning technique that relies on learning texture patterns from markers defined by the user and then classifying each pixel of the image according to its similarity to the learned patterns.



Amira 软件

  • 探索 2D-5D 生物成像数据
  • 识别和理解结构
  • 获取统计信息
  • 分享报告和精彩动画

Avizo 软件

  • 支持多数据/多视图、多通道、时间序列、超大数据
  • 先进的多模式 2D/3D 自动配准
  • 伪影消除算法

Pergeos 软件

  • 可视化、处理和分析岩石图像数据
  • 从孔隙到岩心执行多尺度成像分析
  • 计算地质和岩石物理性质

Athena 软件

  • 确保图像、数据、元数据和实验工作流程的可追溯性
  • 简化您的成像工作流程
  • 促进协作
  • 保护和管理数据访问​




通过专门为 Amira、Avizo 和 PerGeos 软件新用户设计的入门培训,缩短学习曲线,使投资收益最大化。

课程包括一个讲座及互动提问环节。培训材料重点讲述 Amira、Avizo 和 PerGeos 软件的基本特点和功能。



通过专为 Amira、Avizo 和 PerGeos 软件的现有用户设计的高级培训使投资收益最大化并缩短取得成果的时间。

课程包括一个讲座及互动提问环节。培训材料重点讲述 Amira、Avizo 和 PerGeos 软件的高级特点和功能。



赛默飞世尔科技在 3D 和图像处理方面拥有超过 25 年的经验,向众多小型和大型机构交付了数百个定制项目,可根据您的特定需求为您提供量身定制的解决方案。




    • 处理任何规模、任何大小及任何模态的数据:

    - 生物数据格式
    - 位图格式
    - 显微镜:电子和光学
    - 医学和神经图像格式
    - 分子格式
    - 其他图像采集设备(MRI、放射摄影术等)

    • 有限元建模、几何建模、CAD
    • 支持多数据/多视图、多通道、时间序列、超大数据
    • 缩放、校准、转换、重新采样
    • 图像增强、能满足各种需求的滤波和卷积、傅立叶变换
    • 伪影消除算法
    • 先进的多模式 2D/3D 自动配准
    • 图像对齐、算术运算、相关、融合


    • 阈值和自动分割、对象分离、自动标记
    • 区域生长、活动轮廓、插值、卷绕、平滑
    • 形态学处理,包括分水岭和盆地
    • 基于机器学习的分割
    • 自动追踪单个纤维和丝状体
    • 骨架化和纤丝网络提取
    • 交互式工具,用于生成或编辑分割和空间图形
    • 3D 表面重建
    • FEA/CFD 载网生成



    • 动画和视频生成
    • 高级关键帧和物体动画
    • 混合图像、几何模型、测量和模拟
    • 注释、测量图例、柱状图和曲线图
    • 导出电子表格、3D 模型和高质量图像
    • 主动和被动 3D 立体视觉
    • 单屏和多屏显示
    • 沉浸式环境


    • 交互式高质量体渲染和多通道可视化
    • 正交、倾斜、圆柱形和弧形切面
    • 轮廓绘制和等值面提取
    • 最大信号或其他类型投影
    • 矢量和张量可视化
    • 对象和追踪
    • 分子可视化


    • 直观的模板菜单创建、自定义、自动重放
    • 内置测量项目,包括计数、体积、面积、周长、长径比和方向
    • 用户定义的测量指标
    • 内含电子表格工具和图表的结果查看器
    • 自动单个特征测量、3D 定位和电子表格选择
    • 自动统计、分布图
    • 使用任何测量标准进行特征过滤
    • 数据配准、变形、比较和测量


    轻松、快速地调整 Amira 软件以满足您的特定需求

    • 定制 C++ 模块开发
    • MATLAB™ 桥
    • Python 脚本 API

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