Lesion Segmentation
Lesion Segmentation - Related Videos
10:25
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
0 Views •
Cited by 30 •
2019
Provided here is a practical tutorial for an open-access, standardized image processing pipeline for the purpose of lesion-symptom mapping. A step-by-step walkthrough is provided for each processing step, from manual infarct segmentation on CT/MRI to subsequent registration to standard space, along with practical recommendations and illustrations with exemplary cases.
09:26
Preparation and Using Phantom Lesions to Practice Fine Needle Aspiration Biopsies
0 Views •
Cited by 6 •
2009
Practicing of fine needle aspiration biopsies (FNAB) by trainees is relatively challenging, due to the lack of an easily available, appropriate lesion. Preparation of an AV phantom lesion for practicing the FNAB procedure and mastering proficiency is relatively easy.
09:22
Sectioning Mammary Gland Whole Mounts for Lesion Identification
0 Views •
Cited by 13 •
2017
We developed a method to successfully remove, process, section, and stain, for histopathological evaluation, mammary tissue that had originally been fixed on slides as whole mounts. This method may promote the collection and evaluation of mammary gland whole mounts in reproductive and developmental test guideline studies.
08:16
Comparative Lesions Analysis Through a Targeted Sequencing Approach
0 Views •
2019
This article describes a method to identify clonal and subclonal alterations among different specimens from a given patient. Although the experiments described here focus on a specific tumor type, the approach is broadly applicable to other solid tumors.
12:50
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
0 Views •
Cited by 29 •
2014
Lesion Explorer (LE) is a semi-automatic, image-processing pipeline developed to obtain regional brain tissue and subcortical hyperintensity lesion volumetrics from structural MRI of Alzheimer's disease and normal elderly. To ensure a high level of accuracy and reliability, the following is a video-guided, standardized protocol for LE's manual procedures.
FAQs
Lesion segmentation can combine intensity, texture, shape, and anatomical context rather than relying on a single visual cue. These features help distinguish abnormal tissue from surrounding healthy tissue when intensity alone is insufficient. A method may also incorporate machine-learning models, which use image patterns to support classification and improve identification across medical imaging tasks.
Preprocessing prepares medical images for subsequent classification by organizing the image information used during analysis, while boundary assessment checks whether the proposed lesion outline follows the visible abnormal tissue. Together, these stages can refine an initial result instead of treating the first classification as final. This matters because accurate borders support more reliable anatomical measurements.
The same analytical goal can be applied across magnetic resonance imaging, computed tomography, ultrasound, and pathology imaging, but the available visual information differs among these modalities. Consequently, segmentation may draw on different combinations of intensity, texture, shape, anatomical context, or machine-learning outputs. This flexibility allows lesion extent to be evaluated in varied medical imaging settings.
A typical workflow begins with image preprocessing, followed by classification of image pixels or voxels as potential lesion or surrounding tissue. The proposed region is then examined through boundary assessment and refined as needed. This sequence produces an image-based representation that can be measured for anatomical location, extent, and other features relevant to clinical or research analysis.
The resulting segmented region supports measurements such as lesion volume and anatomical location, as well as comparisons of lesion change over time. These measurements convert visual findings into quantitative information that can be evaluated more consistently across images or examinations. Such outputs are useful for tracking disease-related changes and assessing imaging biomarkers in medical research.
Lesion segmentation supports several medical objectives, including diagnosis, disease staging, treatment planning, and monitoring. By quantifying lesion extent and location, it provides information that complements visual image interpretation and allows changes to be followed over time. Researchers can also use these measurements to evaluate imaging biomarkers and compare disease-related findings more systematically.