Include PiPy Package and non-iso pixel spacing

.github/workflows/publish-pkg.yml

@@ -0,0 +1,70 @@
+name: Test and Publish PiPY Package
+
+on:
+  push:
+    tags:
+      - 'v*'
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.9", "3.10", "3.11"]
+
+    steps:
+      - name: Checkout Repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Cache pip dependencies
+        uses: actions/cache@v3
+        with:
+          path: ~/.cache/pip
+          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements*.txt', '**/setup.py', '**/pyproject.toml') }}
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -e .[dev] || pip install -e .
+          pip install pytest
+
+      - name: Run tests
+        run: |
+          pytest --tb=short -v
+
+  build-and-publish:
+    needs: test  # Only run if tests pass
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout Repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.11
+
+      - name: Install build dependencies
+        run: |
+          python -m pip install --upgrade pip build twine
+
+      - name: Build distributions
+        run: |
+          python -m build
+
+      - name: Verify build
+        run: |
+          python -m twine check dist/*
+
+      - name: Publish to PyPI
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
+        run: |
+          python -m twine upload dist/*

.gitignore

@@ -3,7 +3,7 @@ spinenet-venv
 spinenet/weights/*
 tutorials/example_scans/*
 tutorials/results/*
-
+.python-version
 
 # Standard Python GitIgnore
 # Byte-compiled / optimized / DLL files

.readthedocs.yaml

@@ -9,7 +9,7 @@ version: 2
 build:
   os: ubuntu-20.04
   tools:
-    python: "3.8"
+    python: "3.9"
     # You can also specify other tool versions:
     # nodejs: "16"
     # rust: "1.55"

README.md

@@ -1,9 +1,26 @@
+
+
+# SpineNet - PyPI Package
+
+[![PyPI version](https://badge.fury.io/py/spinenet.svg)](https://badge.fury.io/py/spinenet)
+[![Python](https://img.shields.io/pypi/pyversions/spinenet.svg)](https://pypi.org/project/spinenet/)
+[![Tests](https://github.com/mariamonzon/SpineNet/workflows/Test%20and%20Publish%20PiPY%20Package/badge.svg)](https://github.com/mariamonzon/SpineNet/actions)
+
+
+
+SpineNet is now available as a PyPI package for easy installation and use!
+Install SpineNet directly from PyPI using pip:
+
+```bash
+pip install spinenet
+```
+
 <p align="center">
 <img width='100%' src='spinenetlogo.png'> 
 </p>
-
 **DISCLAIMER: SpineNet is not a diagnostics tool nor a medical device. It should only be used for research.**
 
+
 ## Introduction
 
 SpineNet is automated software for analysing clinical spinal MRI scans. Current functionality includes:
@@ -81,7 +98,7 @@ We are grateful to the providers of the example scans used in the tutorials (ori
 ## Coming Soon
 
 - [x] Documentation
-- [ ] Pip installation
+- [x] Pip installation
 - [ ] In-depth tutorials on finetuning SpineNet's grading network
 
 We are always looking to extend SpineNet to make it a more useful tool. If you have a request for features please get in touch with us.

pyproject.toml

@@ -1,3 +1,63 @@
+[project]
+name = "spinenet"
+version = "2.0.4"
+description = "Automated software for analysing clinical spinal MRI scans."
+readme = "README.md"
+authors = [
+    { name = "Maria Monzon", email = "[email protected]" },
+    { name = "Rhydian Windsor", email = "" }
+]
+keywords = ["medical-imaging", "deep-learning", "spine", "MRI", "segmentation"]
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Science/Research",
+    "Topic :: Scientific/Engineering :: Medical Science Apps.",
+    "License :: OSI Approved :: MIT License",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+
+]
+requires-python = ">=3.9,<3.12"
+dependencies = [
+    "opencv-python>=4.5.0,<4.8.0.76",  # Exclude problematic version
+    "numpy<2",
+    "torch>=2.0.0,<=2.5.0",
+    "torchvision>=0.15.0",
+    "numpy>=1.20.0",
+    "scipy>=1.7.0",
+    "scikit-image>=0.19.0",
+    "nibabel>=3.2.0",
+    "pandas>=1.3.0",
+    "pydicom",
+    "shapely",
+    "matplotlib>=3.5.0",
+    "requests",
+    "tqdm"
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=7.0.0",
+    "pytest-cov>=4.0.0",
+    "black>=22.0.0",
+    "ruff>=0.1.0",
+    "mypy>=1.0.0",
+]
+vis = [
+    "matplotlib>=3.5.0",
+    "seaborn>=0.11.0",
+    "plotly>=5.0.0",
+]
+[project.scripts]
+spinenet = "spinenet:main"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+
 [tool.black]
 line-length = 79
 include = '\.pyi?$'
@@ -14,3 +74,4 @@ exclude = '''
   | dist
 )/
 '''
+

requirements.txt

@@ -7,6 +7,7 @@ requests
 scikit-image
 scipy
 shapely
-torch
+torch>=2.0.1,<=2.5.0
 torchvision
 tqdm
+matplotlib

spinenet/.python-version

@@ -0,0 +1 @@
+3.13

spinenet/io/dicom_io.py

@@ -1,3 +1,4 @@
+import logging
 import os
 from typing import List, Tuple, Dict, Union, Any
 import glob
@@ -74,17 +75,18 @@ def load_dicoms(
                 )
             is_sagittal = is_sagittal_dicom_slice(dicom_file)
             if not is_sagittal:
-                raise ValueError(
-                    f"File at {paths[dicom_idx]} is not a sagittal dicom slice"
-                )
+                dicom_files.pop(dicom_idx)
+                #raise ValueError(
+                logging.warning( f"File at {paths[dicom_idx]} is not a sagittal dicom slice")
         # sort slices by sagittal position
         dicom_files = sorted(
             dicom_files, key=lambda dicom_file: dicom_file.InstanceNumber
         )
 
+
         pixel_spacing = np.mean(
             [np.array(dicom_file.PixelSpacing) for dicom_file in dicom_files]
-        )
+        , axis=0)
         slice_thickness = np.mean(
             [np.array(dicom_file.SliceThickness) for dicom_file in dicom_files]
         )

spinenet/utils/detect_and_group.py

@@ -8,7 +8,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import scipy.ndimage
-from .scan_preprocessing import split_into_patches_exhaustive
+from .scan_preprocessing import split_into_patches_exhaustive, split_into_patches_exhaustive_spacing
 from .detection_post_processing import make_in_slice_detections
 
 
@@ -61,8 +61,13 @@ def detect_and_group(
             polygons detected.
     """
 
+
+    # split the scan into different patches
+    # patches, transform_info_dicts = split_into_patches_exhaustive(
+    #     scan, pixel_spacing=pixel_spacing, overlap_param=0.4, using_resnet=using_resnet
+    # )
     # split the scan into different patches
-    patches, transform_info_dicts = split_into_patches_exhaustive(
+    patches, transform_info_dicts = split_into_patches_exhaustive_spacing(
         scan, pixel_spacing=pixel_spacing, overlap_param=0.4, using_resnet=using_resnet
     )
     # group the detections made in each patch into slice level detections

spinenet/utils/detection_post_processing.py

@@ -6,6 +6,32 @@
 from shapely.geometry import Polygon, Point
 
 
+def validate_coordinates(coords, max_shape):
+    """
+    Filter out coordinates that are negative or exceed image bounds.
+
+    Parameters
+    ----------
+    coords : array-like, shape (N, 2)
+        List or array of coordinates to validate.
+    max_shape : tuple of int
+        Maximum allowed shape \(`height`, `width`\) for the coordinates.
+
+    Returns
+    -------
+    valid_coords : ndarray, shape (M, 2)
+        Array of valid coordinates within the image bounds.
+    """
+    if len(coords) == 0:
+        return np.empty((0, 2))
+
+    coords = np.array(coords)
+    valid_mask = (
+            (coords[:, 0] >= 0) & (coords[:, 0] < max_shape[0]) &
+            (coords[:, 1] >= 0) & (coords[:, 1] < max_shape[1])
+    )
+    return coords[valid_mask]
+
 def make_in_slice_detections(
     detection_net,
     patches,
@@ -133,6 +159,9 @@ def make_in_slice_detections(
                         points * patch_edge_len / 224
                         + np.array([transform_info["y1"], transform_info["x1"]])
                     )
+                    transformed_patch_corners = validate_coordinates(
+                        transformed_patch_corners, scan_shape
+                    )
                     all_corners["points"][corner_type].append(transformed_patch_corners)
                     arrows = np.zeros_like(points)
                     for idx, point in enumerate(points):
@@ -171,10 +200,12 @@ def make_in_slice_detections(
         scan_centroid_channel /= centroid_channel_contributions
         # detect the centroids
         centroids = get_points(scan_centroid_channel, threshold=centroid_threshold)
+        centroids = validate_coordinates(centroids, scan_shape)
 
         detection_polys = []
         arrows = all_corners["arrows"]
         corners = all_corners["points"]
+
         # now loop through each detected centroid and find the closest displaced
         # corner of each type
         for centroid in centroids:
@@ -246,7 +277,13 @@ def make_in_slice_detections(
                 elif sum(missing_arrows) == 1:
                     for i, el in enumerate(missing_arrows):
                         if el:
-                            detection_poly[i] = centroid + indiv_arrows[(i + 2) % 4]
+                            new_corner = centroid + indiv_arrows[(i + 2) % 4]
+                            # **VALIDATE NEW CORNER COORDINATE*
+                            if not (0 <= new_corner[0] < scan_shape[0] and 0 <= new_corner[1] < scan_shape[1]):
+                                # Get closest valid coordinate within image bounds
+                                new_corner = np.clip(new_corner, [0, 0], np.array(scan_shape[:2]) - 1)
+                            detection_poly[i] = new_corner
+
                     # # flip around poly to match form needed for matplotlib plotting
                     detection_polys.append([[i[1], i[0]] for i in detection_poly])
 

spinenet/utils/gen_utils.py

@@ -367,7 +367,7 @@ def poly2mask(vertex_row_coords, vertex_col_coords, shape):
     fill_row_coords, fill_col_coords = draw.polygon(
         vertex_row_coords, vertex_col_coords, shape
     )
-    mask = np.zeros(shape, dtype=np.bool)
+    mask = np.zeros(shape, dtype=bool)
     mask[fill_row_coords, fill_col_coords] = True
     return mask
 

spinenet/utils/label_verts.py

@@ -245,18 +245,18 @@ def construct_input_to_context_model(vert_dicts, scan, pixel_spacing):
     box_coords = np.asarray([vert_dict["average_polygon"] for vert_dict in vert_dicts])
     y_centroids = (
         np.asarray([np.mean(box_coord[:, 1]) for box_coord in box_coords])
-        * pixel_spacing
+        * pixel_spacing[1]
     )
     y_maxes = (
         np.asarray([np.max(box_coord[:, 1]) for box_coord in box_coords])
-        * pixel_spacing
+        * pixel_spacing[1]
     )
     y_mins = (
         np.asarray([np.min(box_coord[:, 1]) for box_coord in box_coords])
-        * pixel_spacing
+        * pixel_spacing[1]
     )
     widths = (y_maxes - y_mins) / 2
-    image_height = int(scan.shape[0] * pixel_spacing)
+    image_height = int(scan.shape[0] * pixel_spacing[0])
     height_scaled_appearance_features = np.zeros((int(image_height), 24))
 
     for i in range(len(vert_dicts)):