Application of Masked RCNN for segmentation of brain haemorrhage from Computed Tomography Images

1. Application of Masked RCNN for
segmentation of brain haemorrhage from
Computed Tomography Images
Aditya Bhattacharya
Lead ML Engineer, West Pharmaceuticals
AI Researcher, MUST Research

2. About Me My Associations
My Interests
• Lead ML Engineer, West Pharmaceuticals
• AI Researcher, MUST Research
- ADITYA BHATTACHARYA
Vision Text Speech

3. Objectives of this discussion
Discussions on importance of an AI solution in analyzing Computed Tomography (CT) Scan
Images
Discussions on automated object of interest segmentation using Deep Learning
Using Masked RCNN algorithm to efficiently localize object of interest from images.
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4. Target Audience
AI Researchers
Data Scientists
ML/DL Engineers
AI Enthusiast

5. Topics to be discussed
• Need of an automated AI based solution in CT Scan Image Analysis
• Importance of Deep Learning based solutions for localizing object of interests
within CT Scan images
• Efficient usage of Masked RCNN for segmentation of brain hemorrhage from
CT Scan Images
• Working demo and explanation of how Masked RCNN functions in
segmenting brain hemorrhage

6. Need of an automated AI based solution in CT Scan Image Analysis
Region affected
due to Cerebral
haemorrhage
• Automated analysis of CT scan images using AI solutions to diagnose abnormalities will help in overcoming the
costly, time consuming and prone to error from manual analysis.
• Deep Learning has proved to be quite efficient to
mimic human cognitive abilities (and even exceed
that in many cases), especially with unstructured
data.
• DL algorithms can detect, localize and quantify a
growing list of brain pathologies including intra-
cerebral bleeds and their subtypes, infarcts, mass
effect, midline shift, and cranial fractures.
• So, with advanced DL algorithms, analysis of
radiographic data can be easily achieved and this
can accelerate early detection of certain critical
medical conditions, powered by AI.

7. Importance of DL based solutions for localizing object of interests
within CT Scan images
• Deep Learning algorithms for computer vision use cases has been
extremely successful for classification and localization related problems.
• With the availability of annotated dataset, object of interest or region of
interest segmentation using Deep Learning has been plausible
• Algorithms like Regional Convolutional Neural
Network (RCNN) and it’s evolved forms, Faster RCNN
and Masked RCNN is being widely used in the field
of advanced radiology to auto detect medical
conditions through radio-graphic images.
• For this session, I am particularly going to talk about
application of Masked RCNN for detection of regions
of brain haemorrhage from CT scan images of the
brain.
Original CT Scan Image of brain
Result of Mask RCNN
Algorithm with segmented
region of brain
haemorrhage

8. How exactly does Mask RCNN work for this use case?
** Before we even begin, please refer the original paper: Mask R-CNN by Kaiming He el et. :
https://arxiv.org/pdf/1703.06870.pdf for a more detailed explanation .
Now, why Mask RCNN in this use case, why not anything else?
For detecting brain haemorrhage regions, an actual medical expert would have scanned the entire brain image
and highlight (or mark) one or many areas (if it exists), with almost the exact region affected being marked, with
some amount of certainty.
• The masked RCNN algorithm also functions in a similar way.
• It will scan across the entire image and would create a
coloured mask to highlight exact locations affected with a
certain level of confidence.
• So, typically it does instance segmentation, one of most
difficult problems to solve in Computer Vision.

9. How exactly does Mask RCNN work for this use case?
Mask RCNN is a two stage process:
1. In first stage the entire CT image will be scanned and certain proposals about the regions where the
medical condition (region of interest) can exist.
2. The second stage will classify each proposed region and generate bounding boxes and creates a mask
around the region to highlight.

10. Stage 1: Region Proposal Network in Mask RCNN
The Region Proposal Network (RPN) runs a lightweight binary classifier on a lot of boxes (anchors) over the image
and returns object/no-object scores.
Only positive anchors based on the scores are passed to the stage two to be classified.
Using Non-max suppression and Intersection over Union (IoU) which is an
evaluation metric used to measure the accuracy of an object detector on a
particular dataset, is used to pick the best possible proposals.

11. Stage 2: Proposal Classification in Mask RCNN
This stage takes the region proposals from the RPN and classifies them and generated class probabilities and
bounding box regressions.
1 detections: ['brain_haemorrhage']
And finally application of the generated
mask to highlight the region of interest ->

12. Quick Demo!
Notebook link:
https://tinyurl.com/wgln26q

13. • Lead ML Engineer,
West Pharmaceuticals
• AI Researcher, MUST Research
- ADITYA BHATTACHARYA
Questions?
- Want to connect over LinkedIn ?
- Or email me at: aditya.bhattacharya2016@gmail.com