arkhymadhe / ct-slice-localization Goto Github PK

The dataset comprises 386 features (including ID and target feature, radial axis location) extracted from CT images.

The class variable is of the numeric kind, and denotes the relative location of the CT slice on the axial axis of the human body. The data was retrieved from a set of 53500 CT images from 74 different patients (43 male, 31 female).

Each CT slice is described by two histograms in polar space. The first histogram describes the location of bone structures in the image, the second the location of air inclusions inside of the body. Both histograms are concatenated to form the final feature vector. Bins that are outside of the image are marked with a value of -0.25.

The class variable (i.e. relative location of an image on the axial axis) was constructed by manually annotating up to 10 different distinct landmarks in each CT Volume with known location. The location of slices in between landmarks was interpolated.

• Feature 1 (patientId): Each ID identifies a different patient
• Features 2 to 241: Histogram describing bone structures
• Features 242 to 385: Histogram describing air inclusions
• Feature 386 (target variable; reference): Relative location of the image on the axial axis in degrees (class value). Values are in the range [0; 180] where 0 denotes the top of the head and 180 the soles of the feet.

A link to the dataset may be obtained below.

The task could be modelled either as a regression or classification task. In this instance, the modelling was done via regression. Two models were fit on the dataset: a LinearSVR model, an SVM, and a neural network.

The fully-connected neural network was built via the PyTorch library, for the regression task described in the Overview above. The network was wrapped via the Skorch API, to render it compatible with the Scikit Learn API. The final model was obtained after training for 20 epochs, with a learning rate of 1e-4, and a batch size of 16.

A compressed form of the dataset is provided, with abstractions to decompress and compress as required.

1. Navigate to the scripts folder:

$ cd scripts

2. Ensure compressed data file is decompressed into the data directory

3. Run the main.py file.

$ python3 main.py --arg_key arg_value

4. Arguments available include:

   - epochs
   - task ('classif' or 'regression')
   - lr (learning rate)
   - classes (None if 'regression', int if 'classif')
   - n_features (number of data features)
   - batch_size
   

The performance of the Skorch neural network (~99%) outstripped the vanilla LinearSVR model from Scikit Learn (~84%) via a considerable margin.

An exploratory hypothesis for why this was so might be that the network, by virtue of the RELU non-linearities present, was able to learn non-linear features from the dataset.

Data Source:

1. Dua, D. and Graff, C. (2019). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.

1. Author: F. Graf, H.-P. Kriegel, M. Schubert, S. Poelsterl, A. Cavallaro Source: Dataset - 2011

1. UCI Citation