1. Automatic Lung Nodule Detection Using Deep Learning
REU student: Winona Richey
Graduate student: Naji Khosravan
Professor: Dr. Bagci

2. CAD Lung Nodule Detection
Li F, Arimura H, Suzuki K, et al. Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. Radiology 2005; 237:

3. Problem Overview CT scans: gold standard for lung nodule diagnosis
Nodule detection from CT scans is difficult
CAD systems offer radiologists a second opinion
 Convolutional layers
 accuracy
data required to avoid overfitting
Other factors
Limited training data
Time efficiency

4. Project Overview Hand Crafted Features Data Augmentation Custom CNN
Existing rules for assessing Malignancy
WHO, RECIST
Data Augmentation
Editing existing data
Add noise
Rotate
Optimized Augmentation
Efficiency and Accuracy
Dependent on developed CNN
Custom CNN
Evaluate pre-trained networks
Develop our own CNN: not too deep, not too shallow

5. Custom CNN Develop a CNN to analyze CT scans With higher accuracy
With lower minimum data requirement
fully automated
in 2.5D (takes 2D image slices along x, y and z axes of a 3D input)

6. Tasks Accomplished Familiarization with topic Library installations
Data Preparation
Locate all nodules for each image
Update naming/searching process
Save calculated image coordinates
Output updated, consolidated annotations
Understanding Pre-trained CNN
IP: Layer output evaluation

7. Project Preparation Background Information Programs/Installations
Neural Networks and Deep Learning
Radiography and CT Patterns
Reticular/Linear, Nodular, Consolidation, GGOs, Cavities, Mixtures (Tree-in-Bud)
Feature Extraction
Low level, high level, clinical parameters
Programs/Installations
MATLAB
MatConvNet: Imagenet-vgg-f
Python
TensorFlow, ITK, SimpleITK, SKImage. Basemap,PIL

8. Determining Multiple Nodules
Annotations
Global locations (X, Y, Z)
sizes of nodules (diameter in mm)
Some patients have more than one nodule
Goal: detect all nodules for a patient
Save calculated annotations
Location in image (X, Y, Z)
Add to previous annotations for easy referencing

9. Analyze CNN at each Layer
MATLAB
MatConvNet
Goal:
display the output of each convolutional layer
Process:
Modify existing CNN code
Iterate through each layer
Express layer output as an image
Display and save images