ELMEDILAB

Medical labeling software for machine learning

ELMEDILAB-OVERVIEW-PL.mp4

Table of contents

• 1. Overview
  • 1.1. About the project
  • 1.2. Key features
  • 1.3. Technology stack
  • 1.4. Project summary information
• 2. Application
  • 2.1. Requirements
  • 2.2. Run the application
• 3. Documentation
• 4. Automatic tests

1. Overview

1.1. About the project

The project consists of fully-featured web application which main purpose is to allow proper users to label medical images. The application gathers created annotations in order to use them for developing machine learning algorithms that require qualitative and meaningful data.
This software was evaluated by proper tests and System Usability Scale which estimated that the application is characterized by a high degree of usability.

1.2. Key features

Labeling visualized images	Bounding box type annotation generation
Multiple types of files supported	DICOM, SEQ (thermal imaging FLIR cameras models: SC3000, A655SC, A320G), MP4, PNG, JPEG, JPG, BMP
Exporting annotations	PASCAL VOC XML, COCO JSON, YOLO TXT
Converting annotations formats	PASCAL VOC XML -> COCO JSON, PASCAL VOC XML -> YOLO TXT, COCO JSON -> YOLO TXT
Additional	Managing users, managing submitted annotations, managing tasks and files within a given task

Features availability differ for the normal user. The administrator has wider range of available features.

1.3. Technology stack

Python	Server-side role for processing the application data.
Flask	Web microframework - handling routes, requests and responses.
Jinja2	Template engine for dynamic content loading.
HTML5	Client-side role for web pages construction.
JavaScript	Client-side role for interacting with the application web pages.
CSS, Bootstrap	Styling and designing repsonsive web pages.

1.4. Project summary information

Main Languages

LANGUAGE	FILES	CODE LINES	COMMENT LINES	BLANK LINES	TOTAL LINES
Python	33	2,038	830	463	3,331
HTML + JavaScript	33	2,204	0	127	2,331
CSS	4	487	7	88	582
XML	1	27	0	0	27

Directories

	FILES	CODE LINES	COMMENT LINES	BLANK LINES	TOTAL LINES
export_convert	4	560	160	105	825
task	4	297	127	66	490
labeling	3	300	123	60	483
seq_processing	4	193	69	51	313
admin	3	149	67	37	253
user	4	133	54	30	217
auth	3	129	22	20	171
main	2	14	3	10	27
errors	2	14	32	12	58
templates	34	2,231	0	127	2,358
static	4	487	7	88	582
	71	4,762	844	681	6,287

2. Application

2.1. Requirements

All the requirements are included in this project in the following files: requirements.txt, Pipfile, Pipfile.lock.

Administrator credentials:

• login: admin
• email: admin@admin.com
• password: administrator

2.2. Run the application

In the main project directory, in the command line, run the following commands:

pipenv shell

flask run

Navigate to the http://127.0.0.1:5000 in the browser.

Recommended browsers: Opera, Chrome.

Alternatively, the application can be run from the ELEMDILAB/app.py file (recommended for development purposes).

3. Documentation

In order to get access to the prepared project documentation where all the functions included in this project are properly explained, navigate to the project's root directory (\ELMEDILAB) and in the command line run:

pipenv shell

pipenv run mkdocs serve

Navigate to the http://127.0.0.1:8000 in the browser.

4. Automatic tests

Running prepared tests is possible by navigating to the project's root directory (\ELMEDILAB) and entering in the command line the following command:

pytest

The test raport is available in the /tests/results/pytest_report.html file.

Author: Agata Polejowska, Gdansk University of Technology