DaCapo: a modular deep learning framework for scalable 3D image segmentation (2408.02834v1)

Abstract: DaCapo is a specialized deep learning library tailored to expedite the training and application of existing machine learning approaches on large, near-isotropic image data. In this correspondence, we introduce DaCapo's unique features optimized for this specific domain, highlighting its modular structure, efficient experiment management tools, and scalable deployment capabilities. We discuss its potential to improve access to large-scale, isotropic image segmentation and invite the community to explore and contribute to this open-source initiative.

• The paper introduces DaCapo, a modular deep learning framework that streamlines scalable 3D segmentation for large biological datasets.
• It integrates diverse neural architectures and segmentation strategies, allowing easy switching between semantic and instance segmentation tasks.
• The framework employs blockwise inference and distributed computing to process terabyte-scale datasets using modern file formats and cloud resources.

DaCapo: A Modular Deep Learning Framework for Scalable 3D Image Segmentation

The paper, "DaCapo: a modular deep learning framework for scalable 3D image segmentation", presents a deep learning library specifically designed to simplify and enhance the training and application of machine learning techniques on large, near-isotropic 3D image data. This library, termed DaCapo, aims to address the significant challenges posed by the increasing size and complexity of modern biological imaging datasets.

Introduction

The paper begins by contextualizing the problem faced by researchers in biological imaging: extracting meaningful insights from large, complex datasets. Traditional 2D neural network-based segmentation approaches fall short when applied to modern 3D imaging modalities such as focused ion beam-scanning electron microscopy (FIB-SEM). These methods are not fully optimized for the high-dimensional, near-isotropic nature of such data. To bridge this gap, the authors propose DaCapo, an open-source, modular framework that allows scalable training and deployment of deep learning solutions tailored for these demanding datasets.

DaCapo Framework

DaCapo's standout feature is its modularity, which is meticulously designed to meet a wide array of user needs. The framework supports various submodules that enable the customization of segmentation tasks, handling both 2D and 3D data, and selecting between semantic and instance segmentation. Users can leverage pretrained models or train new models using various neural network architectures. Furthermore, DaCapo is designed to handle terabyte- and teravoxel-scale datasets, deploying segmentation tasks across local, cluster, and cloud infrastructures.

Training Setup

The framework simplifies the training process by managing data segmentation, model checkpointing, and post-processing parameter selection. Users can designate training or validation data subsets through simple tabulated entries (e.g., CSV files). Key steps such as data loading, image augmentation, and model parameter optimization are handled by Gunpowder, integrated within DaCapo. Validation and loss scores are periodically gathered to optimize the model's performance, storing the best iterations and parameters for ease of reference.

Task Specification

DaCapo stands out in its ability to switch between different segmentation tasks with minimal code modifications. It supports a range of prediction targets, including semantic segmentation and instance segmentation. The framework includes designed targets for one-hot encoding, signed tanh boundary distances, and the hot-distance approach. DaCapo's modular design ensures that new prediction targets can be easily integrated, maintaining its state-of-the-art functionality.

Model Architecture

The framework includes multiple pre-built model architectures, including 2D and 3D UNet variants and the Cellpose model. Users can start with pretrained models, such as those developed by the COSEM Project Team, which are available for download and further fine-tuning. These models have demonstrated utility as general-purpose feature extractors for FIB-SEM datasets. Future models by the CeLLMap team and community contributions will be incorporated, enhancing DaCapo's repertoire.

Blockwise Inference and Post-processing

DaCapo employs blockwise inference and post-processing to scale deployment to petabyte-scale datasets. By using chunked file formats like Zarr-V2 and N5, DaCapo can parallelize both semantic and instance segmentation. The framework also supports custom blockwise processing scripts, allowing users to implement tailored post-processing solutions without an in-depth understanding of chunked file formats or parallelization.

Compute Contexts

The framework supports various compute contexts, facilitating both local and distributed training, inference, and blockwise processing. Users can specify the number of CPUs and GPUs and choose the data storage method suited to their project's needs, whether local or cloud-based (e.g., s3, gs, http). DaCapo's design ensures easy integration of custom compute environments, with a provided Docker image for deploying on cloud resources such as AWS.

Conclusion and Future Directions

DaCapo is presented as an invaluable tool for researchers and practitioners dealing with large-volume image data, offering efficient, scalable solutions for biological image segmentation. The framework's design emphasizes adaptability, ensuring long-term utility and model generalization. Future development plans include enhancing the user interface, expanding the repository of pretrained models, and optimizing system scalability.

The paper calls on the research community to engage with DaCapo, encouraging contributions to its ongoing development. This collaborative approach is expected to drive advancements in biological image analysis, supported by DaCapo's robust, flexible framework that meets the demanding needs of modern imaging tasks.

For further engagement and updates, researchers are directed to DaCapo's repository on GitHub. The substantial contributions from the community thus far underscore the framework's collaborative potential and the anticipated advancements it promises to deliver.