ianpan
/

ct-crop

@@ -1,199 +1,136 @@
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 ---
 library_name: transformers
+tags:
+- ct
+- computed_tomography
+- crop
+- dicom
+- radiology
+license: apache-2.0
+base_model:
+- timm/mobilenetv3_small_100.lamb_in1k
+pipeline_tag: object-detection
 ---
+This model crops the foreground from the background in CT slices. It is a lightweight `mobilenetv3_small_100`
+model trained on CT examinations from the [public TotalSegmentator dataset](https://zenodo.org/records/10047292), version.2.0.1.
+The following function was used to generate masks for each CT:
+```
+import nibabel as nib
+import numpy as np
+from scipy.ndimage import binary_closing, binary_fill_holes, minimum_filter
+from skimage.measure import label
+def generate_mask(array):
+    mask = (array > 0).astype("uint8")
+    mask_label = label(mask)
+    labels, counts = np.unique(mask_label, return_counts=True)
+    labels, counts = labels[1:], counts[1:]
+    max_label = labels[np.argmax(counts)]
+    mask = mask_label == max_label
+    mask = np.stack([
+        binary_fill_holes(binary_closing(mask[:, :, i]))
+        for i in range(mask.shape[2])
+    ], axis=2).astype("uint8")
+    mask = np.stack([
+        minimum_filter(mask[:, :, i], size=3)
+        for i in range(mask.shape[2])
+    ], axis=2)
+    return mask
+array = nib.load("ct.nii.gz").get_fdata()
+# apply soft tissue window
+array = apply_ct_window(array, window_level=50, window_width=400)
+mask = generate_mask(array)
+```
+Bounding box coordinates were generated from the masks for individual slices.
+The model was then trained to predict normalized (0-1) `xwyh` coordinates, given an individual CT slice.
+If the mask was empty, the coordinates were set to all zero.
+Images were converted from Hounsfield units (HU) to 4 CT windows:
+1. Soft tissue (level=50, width=400)
+2. Brain (level=40, width=80)
+3. Lung (level=-600, width=1500)
+4. Bone (level=400, width=1800)
+During training, random combinations of channels were selected. If more than 1 channel was selected,
+the images were averaged channel-wise to create a single-channel output. Strong data augmentation was also applied.
+Thus, this model should be robust to different CT windows and combinations thereof.
+Example usage below:
+```
+import cv2
+import torch
+from transformers import AutoModel
+device = "cuda" if torch.cuda.is_available() else "cpu"
+cropper = AutoModel.from_pretrained("ianpan/ct-crop", trust_remote_code=True).eval().to(device)
+# single image
+img = cv2.imread("ct_slice.png", cv2.IMREAD_GRAYSCALE)
+cropped_img = cropper.crop(img, mode="2d", device=device, raw_hu=False, add_buffer=None)
+# expand all 4 sides by 2.5% each
+cropped_img = cropper.crop(img, mode="2d", device=device, raw_hu=False, add_buffer=0.025)
+# expand box height by 2.5% in each direction
+# and box width by 5% in each direction
+buffer = (0.05, 0.025)
+cropped_img = cropper.crop(img, mode="2d", device=device, raw_hu=False, add_buffer=buffer)
+# stack of images
+img_list = ["ct_slice_1.png", "ct_slice_2.png", ...]
+stack = np.stack([cv2.imread(img, cv2.IMREAD_GRAYSCALE) for img in img_list], axis=0)
+cropped_stack = cropper.crop(img, mode="3d", device=device, raw_hu=False, add_buffer=None)
+```
+You can also get the coordinates directly and do the cropping yourself.
+You must separately preprocess the input. Example below:
+```
+# single image
+img0 = cv2.imread("ct_slice.png", cv2.IMREAD_GRAYSCALE)
+img_shapes = torch.tensor([_.shape[:2] for _ in [img0]]).to(device)
+img = cropper.preprocess(img0, mode="2d")
+# if multi-channel, need to convert from channels-last -> channels-first
+img = torch.from_numpy(img).expand(1, 1, -1, -1).float().to(device)
+with torch.inference_mode():
+    coords = cropper(img, img_shape=img_shapes, add_buffer=None)
+# if you do not provide img_shapes, output will be normalized (0-1) coordinates
+# otherwise will be scaled to img_shape
+```
+The model also contains methods to load DICOM images, if you have `pydicom` installed:
+```
+img = cropper.load_image_from_dicom(path_to_dicom_file, windows=None)
+# note: RescaleSlope and RescaleIntercept already applied in the method
+# apply CT window
+brain_window = (40, 80)
+img = cropper.load_image_from_dicom(path_to_dicom_file, windows=brain_window)
+# or multiple windows
+soft_tissue_window = (50, 400)
+img = cropper.load_image_from_dicom(path_to_dicom_file,
+                                    windows=[brain_window, soft_tissue_window])
+# each window is a separate channel, img will be channels-last format
+```
+You can also load a stack of DICOM images from a folder:
+```
+dicom_folder = "/path/to/ct/head/images/"
+# dicom_extension is used to filter files, default is ".dcm"
+# can pass "" if you do not want to filter files
+# default sort is by ImagePositionPatient using automatically determined
+# orientation, can also sort by InstanceNumber
+# can also apply CT windows, as above
+stack = cropper.load_stack_from_dicom_folder(dicom_folder,
+                                             windows=None,
+                                             dicom_extension=".dcm",
+                                             sort_by_instance_number=False)
+# can input raw Hounsfield units into cropper
+cropped_stack = cropper.crop(stack, mode="3d", device=device, raw_hu=True)
+```