Mitosis Detections and Mitotic Network in TCGA

This dataset contains mitosis detections, mitotic network structures, and social network analysis (SNA) measures derived from 11,161 diagnostic slides in The Cancer Genome Atlas (TCGA). Mitoses were automatically identified using the MDFS algorithm [1], and each detected mitosis was converted into a node within a mitotic network. The resulting graphs are provided in JSON format, with each file representing a single diagnostic slide.

JSON Data Format

Each JSON file contains four primary fields:

1. edge_index
   Two parallel lists representing edges between nodes. The ii-th element in the first list corresponds to the source node index, and the ii-th element in the second list is the target node index.

2. coordinates
   A list of [x, y] positions for each node (mitosis). The (x,y) coordinates can be used for spatial visualization or further spatial analyses.

3. feats
   A list of feature vectors, with each row corresponding to a node. These features include:

   • type (an integer representing mitosis type. 1: typical mitosis, 2: atypical mitosis)
   • Node_Degree (the number of nodes connected to the node)
   • Clustering_Coeff (clustering coefficient of the node)
   • Harmonic_Cen (Harmonic centrality of the node)

4. feat_names
   The names of the features in feats. The order matches the columns in each node’s feature vector.

Example JSON Snippet

{
  "edge_index": [[1, 2, 6, 10], [2, 4, 8, 11]],
  "coordinates": [[27689.0, 12005.0], [24517.0, 17809.0], ...],
  "feats": [[1.0, 0.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.115], ...],
  "feat_names": ["type", "Node_Degree", "Clustering_Coeff", "Harmonic_Cen"]
}

Loading Data into NumPy

Below is a sample Python snippet to load one JSON file, extract node coordinates and the type feature, and combine them into a single NumPy array:

import json
import numpy as np

# Path to your JSON file
json_file_path = "example_graph.json"

with open(json_file_path, 'r') as f:
    data = json.load(f)

# Convert coordinates to NumPy
coordinates = np.array(data["coordinates"])

# Identify the "type" column
feat_names = data["feat_names"]
type_index = feat_names.index("type")

# Extract features and isolate the "type" column
feats = np.array(data["feats"])
node_types = feats[:, type_index].reshape(-1, 1)

# Combine x, y, and type into a single array (N x 3)
combined_data = np.hstack([coordinates, node_types])

print(combined_data)

Building a NetworkX Graph

To visualize or analyze the network structure, you can construct a NetworkX graph as follows:

import json
import networkx as nx
import matplotlib.pyplot as plt

json_file_path = "example_graph.json"

with open(json_file_path, "r") as f:
    data = json.load(f)

# Create a NetworkX Graph
G = nx.Graph()

# Add each node with position attributes
for i, (x, y) in enumerate(data["coordinates"]):
    G.add_node(i, pos=(x, y))

# Add edges using the parallel lists in edge_index
# (Adjust for 1-based indexing if necessary)
for src, dst in zip(data["edge_index"][0], data["edge_index"][1]):
    G.add_edge(src, dst)

Visualizing mitotic network using TIAToolbox

Having TIAToolbox installed, one can easily visualize the mitotic network on their respective whole slide images using the following command:

tiatoolbox visualize --slides path/to/slides --overlays path/to/overlays

The only thing to consider is that slides and overlays (provided graph json files) should have the same name. For more information, please refer to Visualization Interface Usage - TIA Toolbox 1.5.1 Documentation.

In case of using this dataset, please cite the following publication:

@article{jahanifar2024mitosis,
  title={Mitosis detection, fast and slow: robust and efficient detection of mitotic figures},
  author={Jahanifar, Mostafa and Shephard, Adam and Zamanitajeddin, Neda and Graham, Simon and Raza, Shan E Ahmed and Minhas, Fayyaz and Rajpoot, Nasir},
  journal={Medical Image Analysis},
  volume={94},
  pages={103132},
  year={2024},
  publisher={Elsevier}
}