How Does Encord Handle Image Segmentation for Computer Vision Projects?

Bounding boxes are fast and forgiving. Segmentation masks are precise and expensive. The difference between a box drawn with 80 percent accuracy and a mask drawn with 80 percent accuracy often determines whether a model works in production — particularly for applications like medical imaging, autonomous driving, and instance segmentation in cluttered scenes.

Getting segmentation annotation right requires both precise tooling and efficient workflow design. Here is how Encord handles it.

TL;DR

• Encord provides polygons, polylines, bitmasks, and keypoints, with SAM 2 natively integrated for single-click mask generation.
• SAM 3 (early access, 2025) extends this with text prompt and class-name click-based annotation for labeling all instances simultaneously.
• SAM performance degrades significantly on satellite imagery, medical imaging, and industrial inspection — domains where the model was not trained.
• Label Studio's open ML backend lets teams connect domain-specific segmentation models rather than relying on vendor-selected integrations.

Encord's segmentation tooling

Encord provides a range of segmentation tools within its annotation interface: polygons, polylines, bitmasks, object primitives, and keypoints. Polygon and polyline tools support vertex-level precision, with keyboard shortcuts for adding, removing, and adjusting vertices.

Bitmask tools allow pixel-level annotation at the pixel boundary rather than approximating with polygon vertices. For tasks requiring tight segmentation boundaries like medical imaging, precision manufacturing inspection, this level of detail matters.

Multi-frame labeling lets objects be labeled across multiple frames simultaneously rather than frame-by-frame, reducing manual effort for static or slow-moving objects in video.

SAM 2 and AI-assisted segmentation

SAM 2 is natively integrated into Encord. For objects with well-defined boundaries in good image conditions, SAM 2 can generate accurate masks in a single click, reducing the time to produce an initial segmentation that human annotators verify and adjust.

SAM 3, in early access as of early 2026, extends this with text prompt and class-name click-based annotation. Annotators can label all instances of a class simultaneously using a natural language prompt or a single click on a class name. For tasks with many instances of the same class, this further reduces per-instance annotation overhead.

Practical efficiency gains from SAM-assisted segmentation depend heavily on image quality and subject type. Clean, well-lit images of well-bounded objects produce large time savings. Cluttered scenes, ambiguous boundaries, and unusual object categories produce smaller ones.

Types of segmentation Encord supports

Semantic segmentation assigns a class label to every pixel in an image, used for scene understanding tasks where the model needs to know what occupies every part of the frame. Common applications include autonomous vehicles, medical tissue classification, and satellite image analysis.

Instance segmentation identifies and separately masks each individual instance of each class. Encord's object tracking in video extends instance segmentation across frames, maintaining object identity over time.

Panoptic segmentation combines semantic and instance approaches. Encord's ontology system can accommodate panoptic schemas, though the workflow design is more complex than pure semantic or instance segmentation.

Medical imaging segmentation via DICOM support with MPR volume annotation allows AI-assisted segmentation on 3D medical volumes. Multi-plane views support annotation of complex anatomical structures across axial, sagittal, and coronal planes.

Limitations to account for

SAM performance varies significantly by image domain. Models trained on natural images perform well on natural image segmentation and degrade on satellite imagery, medical imaging, industrial inspection, and other specialized domains. Teams in these areas should validate SAM quality on representative samples before assuming efficiency gains.

Latency with large cloud datasets is a documented issue in G2 reviews. For workflows involving high-volume image review where reviewers cycle through hundreds of segmentation masks, load delays compound into meaningful throughput losses.

Custom model integration via the SDK is powerful but requires engineering investment. Teams without dedicated ML infrastructure engineers may find the SDK integration more complex than expected.

Label Studio's segmentation approach

Label Studio integrates Segment Anything natively and supports the same core segmentation types: semantic, instance, and custom schemas. The ML backend integration is more open than Encord's: any custom segmentation model can be connected via the ML backend API, giving teams running specialized domain models more flexibility.

For teams where SAM performance on their specific domain is uncertain, Label Studio's open ML backend means the answer is to connect your own model rather than waiting for vendor-supplied integrations. This is particularly relevant for medical, satellite, and industrial imaging domains where general-purpose models frequently underperform domain-specific alternatives.

You can check out our in-depth comparison of Label Studio and Encord here, or talk to an expert at HumanSignal about segmentation workflows for your CV project.