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CITS4402 Computer Vision
创建日期：2024-07-09 16:34:19
标签： CITS4402
Computer Vision

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CITS4402 Computer Vision

Project:  Applications of Computer Vision in Medicine: Magnetic Resonance Imaging Classification for Glioma Diagnosis

Timeline

Week 7 to Week 12: Work on your project. You have 6 weeks. The facilitators will be available in the labs during the lab hours for questions.

Week 12 (21st  & 22nd  of May 2024):  Presentation of the projects during the lab hours. The schedule will be announced.

Grouping

Form groups of 3 students and include the first names and surnames of the group members on LMS Discussion board. This should have been done by now.

Applications of Computer Vision in Medicine: Magnetic Resonance Imaging Classification for Glioma Diagnosis

Background

Medical imaging is a critical application of Computer Vision (CV). In this project, we will apply CV techniques to Magnetic Resonance Imaging (MRI) for glioma diagnosis. Glioma is a type of malignant brain tumor with varying degrees of severity. Gliomas can  be broadly categorized into  Low-Grade Gliomas (LGG) and High-Grade Gliomas (HGG), with LGG being less aggressive and HGG being more aggressive. Accurate diagnosis is crucial for clinical decision-making as the intervention and prognosis significantly differ  between  LGG and  HGG  patients.  Currently, the diagnosis involves  both (i)  non- invasive  imaging  studies  and   (ii)   invasive  histopathological  examinations,  with  the   latter   being essential  for  a  definitive  diagnosis.   However,   invasive  examinations,  such  as   biopsy  or  surgical resection of the tumor tissue,  pose  high  risks  to  patients.  Therefore, there  is  a  pressing  need  to perform. glioma diagnosis solely based on non-invasive imaging studies.

A commonly used medical imaging modality for non-invasive imaging studies is MRI. MRI uses strong magnetic fields and  radio waves to  construct volumetric  images  of the  internal  structures  of the body.  It  is  used  for  imaging  patients  with glioma  due  to  its  ability  to  provide  excellent  contrast between   brain   tumor   and    normal   brain   tissues.   Glioma   exhibits   extreme    heterogeneity   in appearance and shape when visualized on MRI, making image-based diagnosis with the naked eye very challenging. However, LGG/HGG may have certain intrinsic, unique imaging features, suggesting the potential for leveraging CV techniques for image-based diagnosis.

Project Overview

In this project, our objective is to use CV techniques to classify patients with LGG or HGG based on their MRI studies. The project can be divided into the following steps.

•    Step One: Data sourcing;

•    Step Two: Visualization;

•    Step Three: Feature Detection and Extraction;

•    Step Four: Feature Selection;

•    Step Five: Classification using SVM.

Step One: Data Sourcing

The dataset we are  using is  publicly available on  Kaggle. You  need to first  register  on  Kaggle and download the dataset.

The  dataset  contains   MRI  of  369   patients  with  glioma.  An   MRI  volume   is  a  volumetric  image comprising n slices (layers of the volume). Each slice comprises h × w pixels; for the entire volume, there are h × w × nvoxels (pixels in 3D). In this dataset, n=155 for every MRI volume; the MRI slices are stored as H5 files. The filenames follow the pattern volume_[volume ID]_slice_[slice ID], e.g., volume  1  slice_0  suggests the 0th   slice of the 1st  volume. Each H5 file contains four h × w images and three segmentation masks for three tumor sub-regions. The three tumor sub-regions include (i) the necrotic tumor core, (ii) the non-necrotic tumor core, and (iii) the surrounding tissues invaded by the tumor.  For  simplicity,  the  three  segmentation  masks  can  be  merged  to  represent  the  whole tumor; however, you are free to perform. an analysis for each sub-region.

The  four  images  detail  the  same  anatomical   position  and  differ   in  terms  of  image  acquisition protocol. They  can  be  considered  as  four  different  channels  (sequences),  similar  to  the  R,  G,  B channels  of  a  natural  image;  however,  for  subsequent  operation,  they   have  to   be   processed separately. The four images are stored inah × w × 4 array: the 1st, 2nd, 3rd, 4th  (MATLAB)/0th, 1st, 2nd, 3rd   (Python)  ‘layer’  of  the  array  corresponds  to  the  T2  Fluid  Attenuated  Inversion  Recovery  (T2- FLAIR),  native  (T1),  post-contrast  T1-weighted  (T1Gd),  T2-weighted  (T2)  channel,  respectively.  An example  of  the  four  channels  of  the  same  anatomical   position  is  shown   in  Fig.  

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