# Medical Image Segmentation Techniques

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/medical-image-segmentation-techniques/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers covers advances in image segmentation techniques, particularly focusing on medical image analysis, graph cuts, active contours, MRI segmentation, deformable image registration, level set methods, statistical shape models, deep learning, and texture analysis. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t10052 |
| Works | 172 |

## Topic papers all

Showing 15 of 172.

- [An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest](https://scholariq.org/papers/an-automated-labeling-system-for-subdividing-the-human-cerebral-cortex-on-mri/)
- [Whole Brain Segmentation](https://scholariq.org/papers/whole-brain-segmentation/)
- [The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)](https://scholariq.org/papers/the-multimodal-brain-tumor-image-segmentation-benchmark-brats/)
- [Geodesic Active Contours](https://scholariq.org/papers/geodesic-active-contours/)
- [Spatial registration and normalization of images](https://scholariq.org/papers/spatial-registration-and-normalization-of-images/)
- [A hybrid approach to the skull stripping problem in MRI](https://scholariq.org/papers/a-hybrid-approach-to-the-skull-stripping-problem-in-mri/)
- [Assessing the significance of focal activations using their spatial extent](https://scholariq.org/papers/assessing-the-significance-of-focal-activations-using-their-spatial-extent/)
- [Robust anisotropic diffusion](https://scholariq.org/papers/robust-anisotropic-diffusion/)
- [Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets](https://scholariq.org/papers/comparison-and-evaluation-of-methods-for-liver-segmentation-from-ct-datasets/)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://scholariq.org/papers/endonet-a-deep-architecture-for-recognition-tasks-on-laparoscopic-videos/)
- [Texture analysis of medical images](https://scholariq.org/papers/texture-analysis-of-medical-images/)
- [Spatial Normalization of Brain Images with Focal Lesions Using Cost Function Masking](https://scholariq.org/papers/spatial-normalization-of-brain-images-with-focal-lesions-using-cost-function/)
- [Automated cardiovascular magnetic resonance image analysis with fully convolutional networks](https://scholariq.org/papers/automated-cardiovascular-magnetic-resonance-image-analysis-with-fully/)
- [Age-specific CT and MRI templates for spatial normalization](https://scholariq.org/papers/age-specific-ct-and-mri-templates-for-spatial-normalization/)
- [Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics](https://scholariq.org/papers/blood-vessel-segmentation-algorithms-review-of-methods-datasets-and-evaluation/)

## Topic primary papers

Showing 15 of 65.

- [Whole Brain Segmentation](https://scholariq.org/papers/whole-brain-segmentation/)
- [The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)](https://scholariq.org/papers/the-multimodal-brain-tumor-image-segmentation-benchmark-brats/)
- [Geodesic Active Contours](https://scholariq.org/papers/geodesic-active-contours/)
- [Spatial registration and normalization of images](https://scholariq.org/papers/spatial-registration-and-normalization-of-images/)
- [A hybrid approach to the skull stripping problem in MRI](https://scholariq.org/papers/a-hybrid-approach-to-the-skull-stripping-problem-in-mri/)
- [Assessing the significance of focal activations using their spatial extent](https://scholariq.org/papers/assessing-the-significance-of-focal-activations-using-their-spatial-extent/)
- [Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets](https://scholariq.org/papers/comparison-and-evaluation-of-methods-for-liver-segmentation-from-ct-datasets/)
- [Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics](https://scholariq.org/papers/blood-vessel-segmentation-algorithms-review-of-methods-datasets-and-evaluation/)
- [Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound](https://scholariq.org/papers/hough-cnn-deep-learning-for-segmentation-of-deep-brain-regions-in-mri-and/)
- [Evaluation Framework for Algorithms Segmenting Short Axis Cardiac MRI.](https://scholariq.org/papers/evaluation-framework-for-algorithms-segmenting-short-axis-cardiac-mri/)
- [3D whole brain segmentation using spatially localized atlas network tiles](https://scholariq.org/papers/3d-whole-brain-segmentation-using-spatially-localized-atlas-network-tiles/)
- [Lesion identification using unified segmentation-normalisation models and fuzzy clustering](https://scholariq.org/papers/lesion-identification-using-unified-segmentation-normalisation-models-and-fuzzy/)
- [Medical Image Segmentation Methods, Algorithms, and Applications](https://scholariq.org/papers/medical-image-segmentation-methods-algorithms-and-applications/)
- [Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation](https://scholariq.org/papers/learning-with-limited-annotations-a-survey-on-deep-semi-supervised-learning-for/)
- [Evaluation of 3D Correspondence Methods for Model Building](https://scholariq.org/papers/evaluation-of-3d-correspondence-methods-for-model-building/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.