| from pathlib import Path |
| import numpy as np |
| from tools.ct_utils import load_nifti |
|
|
|
|
| def slice_equidistant_select(ct_path, seg_path, n_slices, min_voxels_per_slice=5): |
| if ct_path == seg_path: |
| return "ERROR: Invalid segmentation path, please generate a segmentation map first." |
| try: |
| ct_data, _ = load_nifti(ct_path) |
| except FileNotFoundError: |
| return f"ERROR: CT file not found at {ct_path}" |
| try: |
| seg_data, _ = load_nifti(seg_path) |
| if seg_data.max() > 1 or seg_data.min() < 0: |
| return "ERROR: Invalid segmentation path, please generate a segmentation map first." |
| except FileNotFoundError: |
| return f"ERROR: Segmentation file not found at {seg_path}" |
| seg_mask = seg_data > 0.5 |
| lesion_per_slice = seg_mask.sum(axis=(0, 1)) |
|
|
| all_slice_info = [] |
| for z, n_lesion in enumerate(lesion_per_slice): |
| if n_lesion > min_voxels_per_slice: |
| all_slice_info.append((z, n_lesion)) |
| if not all_slice_info: |
| return "No relevant slices found in the segmentation map." |
|
|
| |
| z_values = sorted(list(set([info[0] for info in all_slice_info]))) |
| discontinuous_groups = [] |
| current_group = [z_values[0]] |
|
|
| for z in z_values: |
| if z - current_group[-1] > 1: |
| discontinuous_groups.append(current_group) |
| current_group = [z] |
| else: |
| current_group.append(z) |
| discontinuous_groups.append(current_group) |
|
|
|
|
| selected_z_indices = [] |
| for group in discontinuous_groups: |
| all_slices_in_group = sorted( |
| [info[0] for info in all_slice_info if info[0] in group] |
| ) |
| indices_to_select = [ |
| int((i / (n_slices + 1)) * len(all_slices_in_group)) |
| for i in range(1, n_slices + 1) |
| ] |
| selected_z_indices.extend( |
| [ |
| all_slices_in_group[idx] |
| for idx in indices_to_select |
| if idx < len(all_slices_in_group) |
| ] |
| ) |
|
|
| selected_z_indices = sorted(selected_z_indices) |
| print("Selected z indices:", selected_z_indices) |
| exported_arrays = [] |
| out_dir = seg_path.parent / seg_path.stem |
| out_dir.mkdir(parents=True, exist_ok=True) |
| for z in selected_z_indices: |
| slice_img = ct_data[:, :, z] |
| out_array = out_dir / f"slice_{z:03d}.npy" |
| exported_arrays.append(out_array) |
| out_array.parent.mkdir(parents=True, exist_ok=True) |
| np.save(out_array, slice_img) |
|
|
| return f"Relevant CT slices files: {[str(p) for p in exported_arrays]}" |
|
|
|
|
| if __name__ == "__main__": |
| from fastmcp import FastMCP |
| from tool_configs import args_tools |
|
|
| args = args_tools() |
| mcp = FastMCP("see", stateless_http=False) |
|
|
| @mcp.tool() |
| async def get_several_slices_tool( |
| image_path: str, segmentation_path: str, n_slices: int = 3 |
| ) -> dict: |
| result = slice_equidistant_select( |
| Path(image_path), Path(segmentation_path), n_slices |
| ) |
| return {"meta": image_path, "outputs": result} |
|
|
| mcp.run(transport="http", host=args.host, port=args.port) |
|
|
