Pixel-level localization
Semantic masks outline tumor sub-regions instead of only indicating that an abnormality exists.
AI-powered neuro-imaging
NeuroSeg AI
Four-Class Brain Tumor Segmentation
A presentation-ready deep-learning workflow for converting multi-modal brain MRI volumes into clear, pixel-level tumor-region maps.
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4Output classes
3Anatomical axes
U-NetSegmentation core
Background
Tumor core
Edema
Enhancing
Clinical presentation value
Why it matters
Turn complex MRI volumes into a visual narrative that is faster to review, easier to explain and more consistent across cases.
Efficient 2D slicing
3D volumes are decomposed into trainable slices across axial, coronal and sagittal views.
Explainable review
Original scan, reference mask and prediction can be reviewed side by side without hiding image boundaries.
Multi-modal input stack
Four complementary MRI sequences
Each sequence emphasizes different tissue characteristics. Together, they give the model richer context for separating tumor regions from normal anatomy.
01T1
Anatomical detail with fluid appearing relatively dark.
02T2
Water-rich tissue and edema appear with stronger signal intensity.
03T1ce
Contrast enhancement highlights regions with blood-brain barrier disruption.
04FLAIR
Suppresses cerebrospinal fluid to improve visibility of edema and lesions.
Source sequence overview
Every modality, shown in full
The original four-sequence reference strip is kept completely visible with object-fit: contain; no scan edges are cropped.
Semantic output map
Four segmentation classes
A simple color system makes the final prediction easier to interpret during a presentation.
Background / healthy tissue
Non-tumorous pixels forming the contextual anatomy around the lesion.
Necrotic & non-enhancing core
Central tumor tissue that does not demonstrate active contrast enhancement.
Peritumoral edema
Fluid-rich tissue surrounding the tumor, especially evident on T2 and FLAIR.
Enhancing tumor
Active tumor tissue highlighted after contrast administration.
Visual evidence
Results you can inspect
Select a view, open it at full size and compare the original slice, reference annotation and predicted segmentation. Every panel uses a non-cropping contain layout.
FLAIR image, ground truth and model prediction are displayed together for two different views.
Original MRI
Preserves the complete source image and native aspect ratio.
Ground truth
Human-provided class labels serve as the reference annotation.
AI prediction
The model outputs a pixel-wise map for side-by-side review.
Model architecture
U-Net: locate what matters, then restore spatial detail
The contracting path learns progressively richer features. The expanding path restores image resolution, while skip connections pass fine spatial information directly to the decoder.
1
Slice 3D MRI volumes along three anatomical axes.
2
Encode tissue patterns through convolution and pooling.
3
Decode features with up-convolution and skip connections.
4
Fuse axis-specific predictions into a tumor-region map.
ARCHITECTURE MAP
01Load MRI volume240 × 240 × 155
→
02Generate 2D slicesAxial · Coronal · Sagittal
→
03Run U-Net modelsShared segmentation logic
→
04Review class mapFour semantic labels
Reported experiment
Training and test indicators
Metrics below reflect the project’s reported improved four-class experiment, presented as research/demo indicators rather than clinical claims.
TRAINING CURVEModel accuracy vs epochs
OPTIMIZATION CURVEModel loss vs epochs
Presentation disclaimer: NeuroSeg AI is presented as a research and demonstration concept. It is not a certified medical device and must not be used as a substitute for qualified clinical interpretation.
Neuro-imaging presentation platform
Smarter segmentation.
Clearer conversations.
A polished, local-first presentation experience designed to demonstrate how AI can support brain MRI review.
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