File size: 5,886 Bytes
d306094 11f63f1 0cd23a0 108b9be 6d29dfb 108b9be 6d29dfb 48504ce 6d29dfb 48504ce ebd4014 48504ce aeee2f4 c87ba1c 48504ce 222046a 28f21da 48504ce aeee2f4 48504ce 6d29dfb 108b9be 2568f65 ebd4014 2568f65 994e313 2568f65 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 |
---
license: mit
language:
- en
pipeline_tag: image-segmentation
tags:
- medical
---
The SegVol is a universal and interactive model for volumetric medical image segmentation. SegVol accepts **point, box, and text prompts** while output volumetric segmentation. By training on 90k unlabeled Computed Tomography (CT) volumes and 6k labeled CTs, this foundation model supports the segmentation of over 200 anatomical categories.
**Keywords**: 3D medical SAM, volumetric image segmentation
## Quicktart
### Requirements
```
conda create -n segvol_transformers python=3.8
conda activate segvol_transformers
```
The [pytorch v1.11.0](https://pytorch.org/get-started/previous-versions/) (or a higher version) is needed. Following install key requirements using commands:
```
pip install 'monai[all]==0.9.0'
pip install einops==0.6.1
pip install transformers==4.18.0
pip install matplotlib
```
### Test script
```python
from transformers import AutoModel, AutoTokenizer
import torch
# get device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# load model
# IF you cannot connect to huggingface.co, you can download the repo and set from_pretrained path as the loacl dir path, replacing "yuxindu/segvol"
clip_tokenizer = AutoTokenizer.from_pretrained("yuxindu/segvol")
model = AutoModel.from_pretrained("yuxindu/segvol", trust_remote_code=True, test_mode=True)
model.model.text_encoder.tokenizer = clip_tokenizer
model.eval()
model.to(device)
print('model load done')
# set case path
# you can download this case from huggingface yuxindu/segvol files and versions
ct_path = 'path/to/Case_image_00001_0000.nii.gz'
gt_path = 'path/to/Case_label_00001.nii.gz'
# set categories, corresponding to the unique values(1, 2, 3, 4, ...) in ground truth mask
categories = ["liver", "kidney", "spleen", "pancreas"]
# generate npy data format
ct_npy, gt_npy = model.processor.preprocess_ct_gt(ct_path, gt_path, category=categories)
# IF you have download our 25 processed datasets, you can skip to here with the processed ct_npy, gt_npy files
# go through zoom_transform to generate zoomout & zoomin views
data_item = model.processor.zoom_transform(ct_npy, gt_npy)
# add batch dim manually
data_item['image'], data_item['label'], data_item['zoom_out_image'], data_item['zoom_out_label'] = \
data_item['image'].unsqueeze(0).to(device), data_item['label'].unsqueeze(0).to(device), data_item['zoom_out_image'].unsqueeze(0).to(device), data_item['zoom_out_label'].unsqueeze(0).to(device)
# take liver as the example
cls_idx = 0
# text prompt
text_prompt = [categories[cls_idx]]
# point prompt
point_prompt, point_prompt_map = model.processor.point_prompt_b(data_item['zoom_out_label'][0][cls_idx], device=device) # inputs w/o batch dim, outputs w batch dim
# bbox prompt
bbox_prompt, bbox_prompt_map = model.processor.bbox_prompt_b(data_item['zoom_out_label'][0][cls_idx], device=device) # inputs w/o batch dim, outputs w batch dim
print('prompt done')
# segvol test forward
# use_zoom: use zoom-out-zoom-in
# point_prompt_group: use point prompt
# bbox_prompt_group: use bbox prompt
# text_prompt: use text prompt
logits_mask = model.forward_test(image=data_item['image'],
zoomed_image=data_item['zoom_out_image'],
# point_prompt_group=[point_prompt, point_prompt_map],
bbox_prompt_group=[bbox_prompt, bbox_prompt_map],
text_prompt=text_prompt,
use_zoom=False
)
# cal dice score
dice = model.processor.dice_score(logits_mask[0][0], data_item['label'][0][cls_idx])
print(dice)
# save prediction as nii.gz file
save_path='./Case_preds_00001.nii.gz'
model.processor.save_preds(ct_path, save_path, logits_mask[0][0],
start_coord=data_item['foreground_start_coord'],
end_coord=data_item['foreground_end_coord'])
print('done')
```
### Training script
```python
from transformers import AutoModel, AutoTokenizer
import torch
# get device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# load model
# IF you cannot connect to huggingface.co, you can download the repo and set from_pretrained path as the loacl dir path, replacing "yuxindu/segvol"
clip_tokenizer = AutoTokenizer.from_pretrained("yuxindu/segvol")
model = AutoModel.from_pretrained("yuxindu/segvol", trust_remote_code=True, test_mode=False)
model.model.text_encoder.tokenizer = clip_tokenizer
model.train()
model.to(device)
print('model load done')
# set case path
# you can download this case from huggingface yuxindu/segvol files and versions
ct_path = 'path/to/Case_image_00001_0000.nii.gz'
gt_path = 'path/to/Case_label_00001.nii.gz'
# set categories, corresponding to the unique values(1, 2, 3, 4, ...) in ground truth mask
categories = ["liver", "kidney", "spleen", "pancreas"]
# generate npy data format
ct_npy, gt_npy = model.processor.preprocess_ct_gt(ct_path, gt_path, category=categories)
# IF you have download our 25 processed datasets, you can skip to here with the processed ct_npy, gt_npy files
# go through train transform
data_item = model.processor.train_transform(ct_npy, gt_npy)
# training example
# add batch dim manually
image, gt3D = data_item["image"].unsqueeze(0).to(device), data_item["label"].unsqueeze(0).to(device) # add batch dim
loss_step_avg = 0
for cls_idx in range(len(categories)):
# optimizer.zero_grad()
organs_cls = categories[cls_idx]
labels_cls = gt3D[:, cls_idx]
print(image.shape, organs_cls, labels_cls.shape)
loss = model.forward_train(image, train_organs=organs_cls, train_labels=labels_cls)
loss_step_avg += loss.item()
loss.backward()
# optimizer.step()
loss_step_avg /= len(categories)
print(f'AVG loss {loss_step_avg}')
# save ckpt
model.save_pretrained('./ckpt')
``` |