Editing Medical Image Segmentation (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Medical image segmentation with nnU-Net:'''
<syntaxhighlight lang="python">
# nnU-Net: self-configuring framework for medical segmentation
# pip install nnunetv2

# Step 1: Prepare dataset in nnU-Net format
# Dataset must be organized as:
# nnUNet_raw/Dataset001_Liver/
#   imagesTr/  -- training images (NIfTI format: .nii.gz)
#   labelsTr/  -- training segmentation masks
#   imagesTs/  -- test images
#   dataset.json  -- metadata file

import json
dataset_info = {
    "name": "LiverTumor",
    "description": "Liver and tumor segmentation from CT scans",
    "reference": "Medical Segmentation Decathlon",
    "licence": "CC-BY-SA 4.0",
    "channel_names": {"0": "CT"},
    "labels": {"background": 0, "liver": 1, "tumor": 2},
    "numTraining": 131,
    "file_ending": ".nii.gz"
}

# Step 2: Plan and preprocess
# nnUNetv2_plan_and_preprocess -d 001 --verify_dataset_integrity

# Step 3: Train (nnU-Net auto-selects architecture: 2D, 3D full res, 3D low res, cascade)
# nnUNetv2_train 001 3d_fullres 0  --npz  (fold 0 of 5-fold CV)

# Step 4: Predict on new data
# nnUNetv2_predict -i /path/to/imagesTs -o /path/to/output \
#     -d 001 -c 3d_fullres --save_probabilities

# Custom PyTorch U-Net for teaching purposes
import torch
import torch.nn as nn

class DoubleConv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv3d(in_ch, out_ch, 3, padding=1), nn.BatchNorm3d(out_ch), nn.ReLU(inplace=True),
            nn.Conv3d(out_ch, out_ch, 3, padding=1), nn.BatchNorm3d(out_ch), nn.ReLU(inplace=True)
        )
    def forward(self, x): return self.conv(x)

class UNet3D(nn.Module):
    def __init__(self, in_ch=1, out_ch=3, features=[32, 64, 128, 256]):
        super().__init__()
        self.encoders = nn.ModuleList([DoubleConv(in_ch if i==0 else features[i-1], features[i]) for i in range(len(features))])
        self.pool = nn.MaxPool3d(2)
        self.decoders = nn.ModuleList([nn.ConvTranspose3d(features[i], features[i-1], 2, stride=2) for i in range(len(features)-1, 0, -1)])
        self.dec_convs = nn.ModuleList([DoubleConv(features[i], features[i-1]) for i in range(len(features)-1, 0, -1)])
        self.head = nn.Conv3d(features[0], out_ch, 1)

    def forward(self, x):
        skips = []
        for enc in self.encoders[:-1]:
            x = enc(x); skips.append(x); x = self.pool(x)
        x = self.encoders[-1](x)
        for up, conv, skip in zip(self.decoders, self.dec_convs, reversed(skips)):
            x = up(x)
            x = torch.cat([x, skip], dim=1)
            x = conv(x)
        return self.head(x)
</syntaxhighlight>

; Medical segmentation tools
: '''Self-configuring''' → nnU-Net v2 (start here for any new task)
: '''Interactive/prompted''' → MedSAM, SAM-Med2D, SAM-Med3D
: '''Universal anatomy''' → TotalSegmentator (117 CT structures), MONAI Label
: '''Pathology (WSI)''' → CLAM, HoverNet (nucleus segmentation), CONCH
: '''Research framework''' → MONAI (Medical Open Network for AI) — PyTorch-based
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