The 🔗 image segmentation involves dividing an image into meaningful regions to facilitate analysis. When interactive, a human guides the algorithm (for example, with advanced annotation tools) to achieve precise segmentation of specific areas of interest. This approach makes it possible to segment any object, even one not anticipated by the classes of an automatic model, thanks to the user's indications. When it comes to preparing datasets, interactive segmentation proves invaluable in filling the gaps left by fully automatic methods, combining the speed of AI with human expertise.
💡 In this article, we explore the principles of interactive segmentation, trace the evolution of techniques (from rule-based methods to neural networks), present its flagship applications (medical imaging, image editing, robotics, etc.), and discuss the current challenges as well as future prospects for this technology.
Interactive segmentation principle
Interactive segmentation involves man-machine collaboration to isolate an object in an image. The user provides visual indications and the segmentation algorithm calculates the corresponding mask (s). Several modes of interaction are commonly used:
- Control points: the user clicks on a few pixels, marking them as belonging either to the target object(positive points) or to the background(negative points). The system then adjusts the mask accordingly, with the user adding further points until the desired result is achieved.
- Bounding box(🔗 bounding box): the user draws an approximate rectangle around the object of interest. The algorithm will then precisely segment the interior of this box, distinguishing the object from the background.
- Scribbles / brushstrokes: the user paints rough lines on the object to be kept and possibly on the background to be excluded. These scribbles serve as a guide for the algorithm to delimit the zones.
Each new indication from the user updates the segmentation iteratively, until the target object is correctly isolated. The great advantage of this approach is that it removes ambiguity in complex cases: the human can specify what the machine should segment. For example, if several objects are touching, or if the lighting disturbs the scene, the user can orientate the result with just a few clicks. In this way, interactive segmentation combines the precision of human control with the computational speed of algorithms, delivering a result that is often more reliable than a fully automatic (or fully manual) method on difficult images.
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Evolution of image segmentation techniques
Image segmentation has evolved considerably over the last few decades, from simple deterministic methods to high-performancedeep learning algorithms. There are three main stages in this evolution:
1. Rule-based methods (1980s-1990s)
Early segmentation processes were based on criteria set manually by image processing experts. These classic techniques included thresholding (binarization of an image according to a luminance or color threshold), edge detection (delimitation of objects via their edges by examining the 🔗 image gradients) or region growing (grouping neighboring pixels with similar characteristics). These "by hand" methods work well in simple cases, but lack robustness as soon as 🔗 scenes are complex or the shooting parameters vary. They often have to be adjusted frame by frame. Nevertheless, they laid the theoretical foundations of segmentation and remain in use for simple needs or pre-processing.
2. Machine learning-based approaches (2000s)
With advances in 🔗 statistical learningresearchers have introduced models capable of learning to 🔗 segment from annotated data. For example, methods combine pixel descriptors (color, texture, etc.) and trained classifiers (SVM, random forests...) to predict the label (object or background) of each pixel. Other techniques, such as random walks or Markov models (MRF/CRF), incorporate neighborhood information to improve segment consistency. In interactive segmentation, an algorithm that marks this era is Graph Cut (and its extension GrabCut), which uses a graph model to interactively separate an object: the user initiates the process (e.g. by roughly surrounding the object) and the algorithm optimizes a cut in the image graph by minimizing a cost criterion. Overall, these approaches partially learn from the data, making them more adaptive than simple fixed rules. However, their performance remains limited by the need to manually define the right features to learn(handcrafted features), and they quickly reach their limits on very complex images or varied objects.
3. Neural networks and Deep Learning (2010s to present)
The revolution came with 🔗 convolutional neural networks (CNNs) capable ofautomatically learning relevant features to segment images. Models such as U-Net, Mask R-CNN or more recently 🔗 Segment Anything (SAM) from Meta have pushed back the frontiers in terms of accuracy and generalizability. By feeding these networks with large sets of annotated images, they manage to finely segment objects of various shapes and sizes, sometimes even under difficult background conditions. Modern techniques often combine encoder-decoder (to capture both global context and local detail) and multi-scale attention, making them highly effective at distinguishing every pixel in the image. What's more, some recent models are promptable, i.e. they accept instructions (points, boxes, text) as input to segment any designated target in the image. This makes them particularly suitable for interactive segmentation, where a point or a click from the user can be used as a prompt to instantly generate a mask.
It's important to note that, despite the excellence of neural networks, traditional methods haven't totally disappeared: in contexts where computing resources are limited or images very simple, well-chosen thresholding may suffice. Nevertheless, for industrial applications requiring robustness and scale, Deep Learning-based approaches dominate image segmentation today.
Applications in various fields
Interactive segmentation has a wide range of applications when it comes to isolating visual objects with precision. It is used to 🔗 annotate data (creating training datasets for AI) as for tools aimed at professionals or the general public. Here are a few major areas where it adds value:
Medicine and biomedical imaging
In medicine, image segmentation is used to delineate anatomical structures or anomalies (tumors, organs, lesions, etc.) on imaging scans (MRI, CT, ultrasound, etc.). Automatic methods are useful, but specialist intervention is often required to correct and refine results. Manually analyzing entire volumes is extremely time-consuming and subject to variations.
Interactive segmentation speeds up this process: a radiologist can, for example, automatically segment a tumor and then correct it in a few clicks if necessary, instead of delimiting it entirely by hand. Similarly, when preparing for computer-assisted surgery, the surgeon can quickly adjust the segmented target area (such as an organ to be treated) to obtain an accurate 3D model. Thanks to these interactive tools, reliable cut-outs of structures of interest can be obtained more quickly, aiding diagnosis, treatment planning or the creation of customized operating guides.
Image editing and graphic design
Whether in photography, advertising or design, interactive segmentation is a valuable tool for manipulating visual elements. A common application is object clipping (or background removal): this involves removing the background from an image to isolate the subject (product, person, etc.). Consumer software such as Photoshop incorporate intelligent selection tools (magnetic lasso, enhanced magic wand, etc.) based on interactive segmentation algorithms: the user indicates the approximate area to be preserved, the tool calculates the precise contour and refines it by painting over poorly cropped areas.
Today, many online services offer one-click AI-assisted background removal. However, they often provide a "manual" mode to adjust the result, as the automatic may confuse elements (for example, fine hair with the background). Interactive segmentation is also used in augmented reality (to dynamically place an object or person in a different setting) or for selective colorization (to isolate a colored element on a black-and-white background, etc.). In all these cases, it offers the user precise control, while eliminating the need to trace contours entirely by hand.
Robotics, autonomous vehicles and machine vision
Robotic systems and autonomous vehicles rely heavily on computer vision to understand their environment. In particular, the 🔗 semantic segmentation provides a fine-grained understanding of each pixel in the image captured by the robot's or car's camera: it assigns each one a label (vehicle, pedestrian, road, obstacle, building...).
This is particularly important for navigation, as the system needs to know where the road is, how to distinguish a pedestrian from a lamppost, and so on. In most cases, these segmentations are performed fully automatically by neural networks trained on thousands of urban images. Nevertheless, the constitution of these training databases makes extensive use of interactive segmentation: human operators manually annotate examples (street images) using interactive tools to segment each object, in order to create 🔗 precise ground truths for training models. On the other hand, in industrial robotics, an operator can use interactive segmentation to quickly teach a robot to identify a particular part among others on an assembly line (by segmenting it over a few images, to generate examples).
We can therefore see that humans intervene either upstream (to produce high-quality annotated data) or possibly in supervision (for example, a driver supervising an autonomous vehicle could correct the detection of an ambiguous obstacle in real time via an interactive segmentation interface, if such assistance functionalities exist in the future). In all cases, interactive segmentation provides quality assurance and a safety net in fields (transport, automation, robotics) where reliability is paramount.
Current challenges and future prospects
Despite its success, interactive segmentation faces a number of challenges. Ideally, we'd like to segment any object with a single click or instruction. Recent work is moving in this direction with foundation models such as Meta's Segment Anything Model (SAM), capable of generating a mask from a simple point or bounding box provided as input. These highly generic models produce impressive results, but they are not infallible. In practice, their predictions still often require human validation and correction. For example, we note that an annotation produced by SAM is not always perfect, and that a specialist has to rework it to achieve the required quality.
Improving first-time accuracy is therefore a key challenge, and will require more efficient networks, possibly combining vision and language (we're beginning to explore models that can be guided by a textual instruction, such as "select the large tree on the right of the image").
On the other hand, interactive segmentation needs to be adapted to new types of data. For example,3D (volumetric)imaging or video pose additional challenges: how can a user effectively guide segmentation in a temporal sequence or volume? Tools need to be invented to propagate corrections over time or across 3D slices, so that humans don't have to go through it all again frame by frame. Other avenues of research involvecontinuous learning: an interactive system could learn from the user's corrections, to avoid repeating the same errors. This is known as adaptive interactive segmentation, where the model is customized to the operator's preferences or to the specific data encountered.
Another challenge lies in theuser experience itself: making the annotation interface as intuitive and efficient as possible. This requires, for example, instant visual feedback (so that the user can see in real time the effect of his clicks), intelligent suggestions (the system could proactively propose to segment a given object if the user hesitates), and the ability to cancel or refine locally without starting all over again. Latency must be kept to a minimum to enable fluid interaction: this means optimizing algorithms (some recent work has focused on lightweight models that can run in real time on CPUs.
Despite these challenges, the outlook for interactive segmentation is very promising. With the rise of ever more powerful and general-purpose AI models, we can imagine tools capable of "segmenting everything" almost instantaneously, requiring only a quick validation by the user. In many professional fields, these advances will save precious time for experts (doctors, engineers, etc.), who will be able to concentrate on analysis rather than on tedious data preparation... even if these tools in no way dispense with the need to set up a complete and efficient labeling process (or LabelOps).
In conclusion, interactive segmentation is a good illustration of the complementarity between humans and AI: algorithms provide speed of execution and the ability to process large volumes of images, while human expertise guarantees the relevance and quality of the final result. Current research efforts are aimed at minimizing the need for intervention without eliminating it altogether, so that the final decision remains in informed human hands. We're confident that in the near future, thanks to the continuous improvement of models and interfaces, interactive segmentation will become an even more transparent and powerful tool, integrating naturally into many workflows without us even realizing it.
Sources for further information
- For a general introduction to the various image segmentation techniques, you may wish to consult 🔗 this article from Innovatiana.
- The 🔗 Kili Technology blog details the principles of interactive segmentation and how it can be used.
- Finally, to discover Meta's Segment Anything model, which foreshadows the future of universal segmentation, we suggest you read 🔗 SAM: everything you need to know.
Happy exploring!
