Ms. Pravda Jith Ray Prasad Profile

Pravda Jith Ray Prasad

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Proceedings Article | 15 February 2021 Presentation + Paper

Modifying U-Net for small dataset: a simplified U-Net version for liver parenchyma segmentation

Pravda Jith Ray Prasad, Ole Jakob Elle, Frank Lindseth, Fritz Albregtsen, Rahul Prasanna Kumar

Proceedings Volume 11597, 115971O (2021) https://doi.org/10.1117/12.2582179

KEYWORDS: Image segmentation, Liver, Surgery, Medical imaging, Image processing, Data modeling, Network architectures, Binary data

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Automation in the field of medical image segmentation is critical in helping the oncologists and surgeons for the accurate analysis of several pathological conditions by saving time. The ability to automatically segment the liver fast and accurately enables clinicians to understand the anatomical structure of the organ, and helps in the decision making process of diagnosis, surgery planning and as an anatomical map during surgical navigation especially important when using intraoperative image modalities . This work aims to develop an automatic liver parenchyma segmentation network which is based on U-Net architecture, a widely used architecture for medical image segmentation. This modified U-Net architecture includes reduced convolutional layers and using dropout layers as well as pre-processing the dataset to overcome the constraints of a small sample set. Reduced architecture complexity and introducing dropout regularization, addresses the problem of overfitting. We experimented with a callback for observing the training failure where it follows the early stopping policy and selecting the best model. Adding Gaussian noise to data can help the model to generalise well. For choosing the appropriate loss function we tested four different loss functions; Dice, binary cross entropy, Tversky and focal Tversky and concluded that Dice performs better. The network has been trained and validated using publicly available 3D-IRCADb dataset with images from 20 patients and achieved an overall Dice score of 94.5%. The overall objective of this work is to construct a network from a small sample set without the problem of overfitting or under-fitting, but delivering an acceptable Dice score.

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