[PDF] Medical SAM 2: Segment medical images as video via Segment Anything Model 2 | Semantic Scholar (2024)

FAQs

Is segment anything model sam in medical image segmentation tasks accurate? ›

SAM's accuracy for zero-shot medical image segmentation is moderate on average and varies significantly across different datasets and different images within a dataset.

How does medical image segmentation work? When working with CT, MRI, and other types of scans, segmentation generally works by taking information from the background image data and using it to generate a mask. Depending on the task, users may work on their scans in 2D or 3D.

Medical image segmentation has automatic or semiautomatic detection of the two-dimensional (2D), or three-dimensional (3D), image. Image segmentation is the procedure of dividing a digital image into a multiple set of pixels.

Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, often tailored to specific modalities or disease types, lack generalizability across the diverse spectrum of medical image segmentation tasks.

Threshold-based segmentation, graph-based segmentation, morphological-based segmentation, edge-based segmentation, clustering-based segmentation, Bayesian-based segmentation, and neural network-based segmentation are some of the best image segmentation models.

Segmentation vs Detection: When to Choose Each

Both approaches have their strengths and specific use cases, making it important to understand when to choose each one. Segmentation is the preferred choice when the task requires a comprehensive understanding of object boundaries and extracting fine-grained information.

Deep learning models have demonstrated great promise in the field of medical image segmentation due to their ability to learn intricate imaging features. However, existing methods are often tailored for specific modalities or targets, which constrains their ability to effectively generalize across different tasks.

The Segmentation IOD specifies a multi-frame image representing a classification of pixels in one or more referenced images. Segmentations are either binary or fractional.

For example, a semantic segmentation model trained to identify certain classes on a city street would yield segmentation masks indicating the boundaries and contours for each relevant class of thing (like vehicles or light poles) and stuff (like roads and sidewalks), but not make any distinction between (or count the ...

How Image Segmentation Works. Image segmentation involves converting an image into a collection of regions of pixels that are represented by a mask or a labeled image.

What is image and video segmentation? ›

It involves partitioning a digital image into multiple segments (regions or objects) to simplify and analyze an image by separating it into meaningful components, Which makes the image processing more efficient by focusing on specific regions of interest.

Segmentation provides fine-grained information about object boundaries and regions, while detection focuses on identifying specific objects and their locations. Classification assigns labels to images or regions, providing a holistic understanding of content.

Advantages of image segmentation include easy data storing, faster processing, and a multiple copy system. Disadvantages include the need for extreme precision and error-prone results. The paper discusses the advantages and disadvantages of image segmentation techniques in machine vision.

As a healthcare organization, it's crucial to segment your patient populations as it will help you identify high-risk, high-cost patients, understand the complex care needs of various patient groups, and tailor care delivery or engagement efforts based on those specific needs.

Radiology Market: Segmentation

The global radiology market is segmented based on services, type, end-user, and region. Based on services, the global market is segmented into X-rays, fusion imaging, nuclear medicine, ultrasound, positron emission tomography (PET), computed tomography, and magnetic imaging (MRI).

It can be assessed qualitatively through visual interpretation or quantitatively by comparing the segmentation result with a reference dataset and measuring the overlapping area or specific characteristics of the segments.

Some popular deep learning models for semantic segmentation include Fully Convolutional Network (FCN), U-Net, DeepLab, and PSPNet. These models leverage the power of convolutional neural networks (CNNs) to extract features and classify pixels in complex images accurately.

Segmentation aims to categorise the population according to health status, health care needs and priorities. This approach recognises that groups of people share characteristics that influence the way they interact with health and care services.

Broadly speaking, image segmentation is used for three types of tasks: semantic segmentation, instance segmentation and panoptic segmentation. The difference between each type of image segmentation task lies in how they treat semantic classes: the specific categories a given pixel might be determined to belong to.