LungNeuralNet

We demonstrated that the CNNs, including U-Net and Mask R-CNN, are instrumental to provide:

• efficient evaluation of pathological lung lesions.
• detailed characterization of the normal lung histology.
• precise detection and classification for BALF cells.

Overall, these advanced methods allow improved efficiency and quantification of lung cytology and histopathology.

Applications of U-Net like architectures

The convolutional neural network architecture used in this project was inspired by U-Net and dual frame U-Net with added transfer learning from pre-trained models in keras (keras-applications).

Lung Pathology

After training on 14 image pairs, the neural network is able to reach >90% accuracy (dice coefficient) in identifying lung parenchymal region and >60% for severe inflammation in the lung in the validation set.
The prediction results on a separate image, including segmentation mask and area stats, was shown below.

Multi-label overlay (blue: parenchyma, red: severe inflammation)

Lung Histology

After training and validating (3:1) on 16 whole slide scans, the neural network is able to identify a variety of areas in a normal mouse lung section (equivalent to 10X, cropped from whole slide scan).

Variations of U-Nets were built to perform

• single-class segmentation
  • output: sigmoid
  • loss: dice & binary crossentropy
  • metrics: dice
• multi-class segmentation
  • output: softmax
  • loss: multiclass crossentropy
  • metrics: accuracy

Among them, dual-frame slightly outperform U-Net with single-frame.
Although more time consuming, single-class segmentation combined with argmax achieved a better classification results than those done by one multi-class segmentation model,
especially for the underrepresented categories.

The best results are listed below:

• single-class segmentation (dice)
  • background: 97%
  • conducting airway: 84%
  • connective tissue: 83%
  • large blood vessel: 78%
  • respiratory airway: 97%
  • small blood vessel: 63%
• multi-class segmentation (accuracy)
  • all six categories: 96%

These methods are helpful for identifing and quantifing various structures or tissue types in the lung and extensible to developmental abnormality or diseased areas.

Non-Parenchymal Region Highlighted Image

Six-Color Segmentation Map

Applications of Mask R-CNN

Mask RCNN was developed by Kaiming He, 2017 to simultaneously perform instance segmentation, bounding-box object detection, and keypoint detection.

This project was based on the implementation of matterport with additional functionalities:

• support more convolutional backbone, including vgg and densenet.
• support large images by performing slicing and merging images & detections.
• simulate bronchoalveolar lavage from background & representative cell images for efficient training.
• batch-evaluate models for the mean average precision (mAP).

Broncho-alveolar Lavage Fluid Cytology

After training and validating (3:1) on 21 background image with 26 lymphocytes, 95 monocytes, and 22 polymorphonuclear leukocytes, the neural network is able to detect and categorize these cell types in a mouse lung bronchoalveolar lavage fluid (20X objective).

Within one day of training, the accuracy represented by mean average precision has reached 75% for all categories.
The accuracy is highest for the monocyte category.

Data credits: Jeanine D’Armiento, Monica Goldklang, Kyle Stearns; Columbia University Medical Center