Files

root b30a4f0d32 Diagram generation: ref-arch-driven procedure + spec validator + KB enrichment

The diagram path now follows a documented standard procedure (lookup
the closest Oracle Architecture Center reference → confirm components
→ author absolute_layout → spec validator → render → visually verify)
and ships persistent guardrails so layout regressions can't recur.

Persistent procedure changes (apply to all users, all sessions):
- tools/diagram_spec_validator.py — geometry checks (CONTAINER_TOO_THIN,
  CONTAINER_PADDING_VIOLATION, LABEL_OVERFLOW_PARENT) run BEFORE either
  renderer (drawio + PPTX). Catches the subnet-collapse / label-overflow
  bugs that the post-render drawio validator missed.
- tools/oci_diagram_gen.py + tools/oci_pptx_diagram_gen.py — call the
  spec validator before emitting any output. Adds mysql / mysql_heatwave
  type aliases.
- tools/archcenter_pattern_lookup.py — scores against cached page
  descriptions (not just the 1-line summary), supports --queries for
  multi-fragment composition, and applies synonym expansion via
  kb/architecture-center/synonyms.yaml so "LB HA cross AD" matches
  "load balancer high availability availability domain".
- kb/architecture-center/synonyms.yaml — canonical synonym table
  (load balancer, autonomous database, data guard, …) used by the
  lookup scorer.

KB enrichment:
- tools/archcenter_description_fetcher.py + 121 cached _description.md
  under kb/diagram/assets/archcenter-refs/<slug>/. Removes the runtime
  dependency on docs.oracle.com when authoring specs and feeds the
  pattern-lookup scorer.
- 110+ cached .drawio / .svg / .png references for offline reuse,
  plus the OCI Toolkit v24.2 import (kb/diagram/assets/oci-toolkit-drawio).

Documentation:
- docs/skill/output-formats.md — new "Standard diagram-generation
  procedure (MANDATORY)" + geometry rules + the new validator entry.
- SKILL.md option 2 — references the mandatory procedure.
- README.md — describes the spec validator, archcenter_pattern_lookup
  and description fetcher, and updates the KB-health table.

Tooling that backs the procedure (cumulative across recent sessions):
tools/archcenter_case_runner.py, archcenter_batch_driver.py,
archcenter_zip_downloader.py, drawio_visual_validator.py,
drawio_fidelity_eval.py, harvest_drawio_icon.py, import_oci_library.py,
oci_pptx_diagram_gen.py, oci_pptx_render.py, refresh_pptx_icon_index.py.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-04-25 21:15:21 -03:00

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Implement a cloud-native DICOM store on Oracle Cloud Infrastructure

Source: https://docs.oracle.com/en/solutions/cloud-native-dicom-on-oci/index.html
Date: 2025-12
Type: reference-architecture
Services: oke, object-storage, adb-s
Tags: healthcare, application, autonomous

Summary (catalog)

Cloud-native DICOM store on OKE with Object Storage for medical images and ADB-S for metadata. DICOMweb API compliance for interoperability with medical imaging systems.

Architecture (fetched from source)

Architecture

This architecture implements a DICOM store on Oracle Cloud Infrastructure .

The following diagram illustrates this reference architecture.

Description of the illustration dicom-oci-diagram.png

oci-dicom-store-oracle.zip

The architecture has the following components:

Oracle Cloud Infrastructure Oracle Cloud Infrastructure (OCI) is Oracle's cloud computing platform that provides a comprehensive suite of services for building, deploying, and managing applications in the cloud. Designed for enterprise performance and security, OCI offers infrastructure as a service (IaaS), platform as a service (PaaS), and other cloud services such as AI, machine learning, networking, storage, and database solutions.

OCI provides high performance, scalability, and cost-efficiency, supporting a wide range of workloads from legacy enterprise applications to cloud-native services. It is ideal for businesses seeking robust cloud capabilities with strong support for hybrid and multi-cloud architectures.

Cloud-based data science platform Oracle Cloud Infrastructure Data Science is a fully managed platform that enables data scientists to build, train, deploy, and manage machine learning models at scale. It provides a collaborative, secure environment with integrated tools for the entire data science lifecycle, including notebooks, automated model training, model evaluation, and deployment.

Data Science supports popular open-source frameworks like TensorFlow, scikit-learn, PyTorch, and XGBoost, and offers seamless integration with other OCI services such as Object Storage, Data Flow, and AI Services. Backed by a wide selection of CPU and GPU shapes, Data Science helps organizations accelerate AI development, streamline collaboration across teams, and operationalize machine learning models efficiently in the cloud.

AI model training and inference AI training and inference are the two core stages in the lifecycle of an artificial intelligence model:
Training is the process by which a model learns from large datasets by adjusting its internal parameters to recognize patterns, make predictions, or perform specific tasks. This phase is computationally intensive and requires powerful hardware such as GPUs, as well as high-throughput storage and networking.
Inference is the stage where the trained model is used to make predictions or decisions based on new, unseen data. Inference typically needs to be fast and scalable, especially in real-time applications like medical imaging, fraud detection, or virtual assistants.

Chosen by world’s leading AI companies such as OpenAI and xAI, OCI Generative AI infrastructure has emerged as a leading platform for AI model training and inference, offering a unique combination of performance, scalability, and cost-efficiency.

Security and Compliance OCI offers a comprehensive set of security and compliance features designed to protect data, applications, and workloads across the cloud environment. OCI provides built-in security at every layer, including network security, identity and access management (IAM), data encryption, threat detection, and monitoring.

Key features include isolated network virtualization, customer-controlled encryption keys, security zones, and Oracle Cloud Guard for continuous monitoring and automated threat response. OCI also supports compliance with major industry and regulatory standards such as ISO, SOC, HIPAA, GDPR, and FedRAMP, helping organizations meet strict data protection and governance requirements.

These capabilities make OCI a trusted platform for running sensitive, mission-critical workloads securely and in compliance with global standards.

Orthanc Orthanc is an open-source, lightweight DICOM server designed to manage, store, and share medical imaging data. It is widely used in hospitals and research labs, and by developers to build imaging workflows without relying on heavy commercial PACS (Picture Archiving and Communication Systems).

Orthanc seamlessly integrates with OCI Object Storage and OCI Database with PostgreSQL , providing a DICOMweb API endpoint that enables users to store, retrieve, and query DICOM images by using RESTful APIs.

In this reference architecture, Orthanc is deployed on OCI Container Instances or OCI Container Instances (OKE), eliminating the infrastructure management overhead for our healthcare customers.

OCI Object Storage Actual DICOM images are stored in an OCI Object Storage bucket.

OCI Object Storage offers highly durable, infinitely scalable, and secure storage for any type of data—structured or unstructured. Whether you're archiving medical images, serving media files, or backing up enterprise workloads, OCI Object Storage provides low-latency access, built-in redundancy, and tiered pricing to optimize cost and performance. It is designed for modern cloud applications, integrates natively with AI, analytics, and DevOps tools, and offers seamless data lifecycle management, all with no up-front hardware investment.

OCI Database with PostgreSQL In this reference architecture, Orthanc is integrated with OCI Database with PostgreSQL to manage and index the non-image metadata associated with DICOM files. While Orthanc can run without an external database (using its built-in SQLite engine), integrating with a managed and more powerful database such as OCI Database with PostgreSQL is recommended for scalability and performance.
OCI Kubernetes Engine Oracle Cloud Infrastructure Kubernetes Engine ( OCI Kubernetes Engine or OKE ) is a fully managed, enterprise-grade Kubernetes service that simplifies container orchestration on Oracle Cloud. With OKE, you get automated provisioning, scaling, and updates, so you can deploy, run, and manage cloud-native applications with less overhead.

Running AI workloads on OKE gives you the best of both worlds: the flexibility of containers and Kubernetes, combined with the power of OCI’s high-performance infrastructure. Whether you're training models on GPU nodes or deploying inference at scale, OKE lets you manage AI pipelines with ease using your favorite tools like TensorFlow, PyTorch, or Hugging Face.

With automated scaling, GPU support, integrated logging, and cost-efficient pricing, OKE makes it simple to build, run, and scale AI workloads in production, all in a secure, enterprise-grade environment.

OCI FastConnect OCI FastConnect offers dedicated, private network connectivity between customer’s on-premises facility, such as a hospital, and Oracle Cloud , delivering high throughput, low latency, and predictable performance for enterprise workloads. It bypasses the public internet entirely, improving security and reliability for hybrid and multicloud architectures.

But what sets OCI FastConnect apart is its cost model: OCI doesn’t charge for data egress over Oracle Cloud , unlike other cloud providers that often impose steep outbound data transfer fees. This creates significant cost savings for data-heavy workloads like medical imaging.

OCI Roving Edge Device OCI Roving Edge Devices (REDs) help facilitate DICOM data transmission and provide the necessary infrastructure for local inference. They bring the power of Oracle Cloud to the edge: rugged, portable, high-performance nodes that let you run applications, process data, and deploy AI models in disconnected or remote environments.

REDs enable low-latency edge computing with full compatibility with OCI services. You can preload VMs, containers, and data, then synchronize with the cloud when connectivity is available.

Recommendations

Use the following recommendations as a starting point for implementing a DICOM store on Oracle Cloud Infrastructure . Your requirements mi

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Implement a cloud-native DICOM store on Oracle Cloud Infrastructure

Summary (catalog)

Architecture (fetched from source)

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