john
a01fe8a35c
✅ New Features Graphical file selectors for both: Choosing the discovery output file Choosing the name and location of the analysis output file Output saved to a text file formatted for easy copy/paste into Excel Tab-separated values in the summary for compatibility with Excel columns Modality breakdown + exam statistics in one concise report 📝 How to Use It Save the code below as dicom_analysis_tool.py. Run it (python dicom_analysis_tool.py or double-click if .py is registered). Click the button to: Select the discovery results .txt file. Specify the output .txt destination. It will create a text summary that can be easily opened in Excel (tab-delimited).
84 lines
3.3 KiB
Python
84 lines
3.3 KiB
Python
import tkinter as tk
|
|
from tkinter import filedialog, simpledialog
|
|
import pandas as pd
|
|
import os
|
|
|
|
def normalize_modalities(val):
|
|
if val.startswith("[") and val.endswith("]"):
|
|
try:
|
|
return ", ".join(eval(val))
|
|
except:
|
|
return val
|
|
return val
|
|
|
|
def analyze_and_save():
|
|
input_path = filedialog.askopenfilename(
|
|
title="Select DICOM Discovery Output File",
|
|
filetypes=[("Text Files", "*.txt")]
|
|
)
|
|
if not input_path:
|
|
return
|
|
|
|
output_path = filedialog.asksaveasfilename(
|
|
title="Save Analysis Output As",
|
|
defaultextension=".txt",
|
|
filetypes=[("Text Files", "*.txt")]
|
|
)
|
|
if not output_path:
|
|
return
|
|
|
|
df = pd.read_csv(input_path, delimiter='|', dtype=str).fillna('')
|
|
df['ModalitiesInStudy'] = df['ModalitiesInStudy'].apply(normalize_modalities)
|
|
|
|
stats = {
|
|
"Distinct StudyInstanceUIDs": df['StudyInstanceUID'].nunique(),
|
|
"Distinct AccessionNumbers": df['AccessionNumber'].nunique(),
|
|
"AccessionNumbers with 2 StudyInstanceUIDs": (df.groupby('AccessionNumber')['StudyInstanceUID'].nunique() == 2).sum(),
|
|
"AccessionNumbers with 3 StudyInstanceUIDs": (df.groupby('AccessionNumber')['StudyInstanceUID'].nunique() == 3).sum(),
|
|
"AccessionNumbers with >3 StudyInstanceUIDs": (df.groupby('AccessionNumber')['StudyInstanceUID'].nunique() > 3).sum(),
|
|
"Missing AccessionNumbers": (df['AccessionNumber'] == '').sum(),
|
|
"Missing StudyDescriptions": (df['StudyDescription'] == '').sum(),
|
|
"*QA* in PatientID": df['PatientID'].str.contains('QA', case=False, na=False).sum(),
|
|
"*QA* in StudyDescription": df['StudyDescription'].str.contains('QA', case=False, na=False).sum(),
|
|
"*QA* in PatientName": df['PatientName'].str.contains('QA', case=False, na=False).sum(),
|
|
"*TEST* in PatientID": df['PatientID'].str.contains('TEST', case=False, na=False).sum(),
|
|
"*TEST* in StudyDescription": df['StudyDescription'].str.contains('TEST', case=False, na=False).sum(),
|
|
"*TEST* in PatientName": df['PatientName'].str.contains('TEST', case=False, na=False).sum()
|
|
}
|
|
|
|
modality_counts = {
|
|
"CR": 0, "DX": 0, "CT": 0, "MR": 0, "NM": 0, "US": 0,
|
|
"XA": 0, "PR": 0, "SC": 0, "OT": 0, "Other": 0
|
|
}
|
|
|
|
for val in df['ModalitiesInStudy']:
|
|
for modality in [m.strip().upper() for m in val.split(',') if m.strip()]:
|
|
if modality in modality_counts:
|
|
modality_counts[modality] += 1
|
|
else:
|
|
modality_counts["Other"] += 1
|
|
|
|
with open(output_path, 'w') as f:
|
|
f.write("DICOM Discovery Analysis Summary\n")
|
|
f.write("="*40 + "\n\n")
|
|
for key, value in stats.items():
|
|
f.write(f"{key}\t{value}\n")
|
|
f.write("\nModality Breakdown\n")
|
|
f.write("-"*40 + "\n")
|
|
for modality, count in modality_counts.items():
|
|
f.write(f"{modality}\t{count}\n")
|
|
|
|
tk.messagebox.showinfo("Analysis Complete", f"Analysis saved to:\n{output_path}")
|
|
|
|
# GUI
|
|
root = tk.Tk()
|
|
root.title("DICOM Discovery Analyzer")
|
|
root.geometry("400x150")
|
|
|
|
frame = tk.Frame(root, padx=10, pady=20)
|
|
frame.pack(expand=True)
|
|
|
|
tk.Button(frame, text="Select Input File and Analyze", command=analyze_and_save, width=30).pack(pady=10)
|
|
|
|
root.mainloop()
|