Semi-supervised learning for multi-modal bone segmentation

Type: BA thesis

Status: running

Date: August 1, 2022 - January 1, 2023

Supervisors: Mingxuan Gu, Andreas Maier

Since AlexNet won the ImageNet Challenge by a wide margin in 2012, the popularity of deep learning has been steadily increasing. In the last years, a technique that has been especially popular is semantic segmentation, as it is used in self-driving cars and medical image analysis. A big challenge that arises when training neural networks (NN) for this task is the acquisition of adequate segmentation masks, because the labeling has often times to be performed by industry experts and is very time consuming. Resulting from that, solutions circumventing this problem had to be found. A popular solution for this task is semi-supervised learning, where only a certain amount of the data is annotated. This approach has the obvious advantage of reducing the time needed for the data acquisition process, but NNs trained this way still have a worse performance compared to ones that were trained fully-supervised.

A common disease affecting one in three women and one in twelve men is osteoporosis. It’s symptoms include low bone mass and a deterioration of bone tissue, leading to an increased fracture risk. The malady affects especially elderly people and for their protection, providing diagnostic tools and suitable treatments is important [1]. Structures that can be found in the bone include lacunae containing osteocytes and trans-cortical vessels (TCV). Murine and human tibia consists of two parts; the inner trabecular bone and the outer cortical bone, where TVCs can be found. To study them and their importance for the development of osteoporosis, we are trying to automatically segment the cortical bone from the surrounding tissue. Additionally, we will attempt to build a NN for the detection of TVCs and lacunae.

We want to achieve this using a model based on convolutional neural networks (CNN) for semantic segmentation. Similar tasks have already been performed [2], but our approach differs as we try to use as few labels as possible for the training process. Methods we want to incorporate are pre-training and the use of image transformations to make the most out of a limited amount of segmentation masks. If those approaches do not yield the desired results, we will also try to incorporate techniques of weakly- and self-supervised learning.

In detail, the thesis will consist of the following parts:

• implementation of multiple CNN-based architectures [3][4] to find a suitable model for our task,

• optimization of this model using different approaches,

• evaluation of the usefulness of pre-training and different semi-supervised learning techniques,

• integration of different techniques to increase the accuracy

References
[1] S P Tuck and R M Francis. Osteoporosis. Postgraduate Medical Journal, 78(923):526–532, 2002.
[2] Oliver Aust, Mareike Thies, DanielaWeidner, FabianWagner, Sabrina Pechmann, Leonid Mill, Darja Andreev, Ippei Miyagawa, Gerhard Kronke, Silke Christiansen, Stefan Uderhardt, Andreas Maier, and Anika Gruneboom. Tibia cortical bone segmentation in micro-ct and x-ray microscopy data using a single neural network. In Klaus Maier-Hein, Thomas M. Deserno, Heinz Handels, Andreas Maier, Christoph Palm, and Thomas Tolxdorff, editors, Bildverarbeitung fur die Medizin 2022 , pages 333–338, Wiesbaden, 2022. Springer Fachmedien Wiesbaden.
[3] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. CoRR, abs/1505.04597, 2015.
[4] Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. CoRR, abs/1411.4038, 2014.
[5] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alche-Buc, E. Fox, and R. Garnett, editors, ´ Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc., 2019