Semi-Supervised Segmentation of Cell Images using Differentiable Rendering.

Type: BA thesis

Status: finished

Date: October 1, 2020 - March 1, 2021

Supervisors: Andreas Maier, Leonid Mill

With the recent advancements in machine learning and mainly deep learning [1], deep convolutional neural networks
(CNNs) [2–7] have been developed, which are able to learn from data sets containing millions of images [8] to resolve
object detection tasks. When trained on such big data sets, CNNs are able to achieve task-relevant object detection
performances that are comparable or even superior to the capabilities of humans [9, 10]. A key problem of using deep
learning for cell detection is in general the large amount of data needed to train such networks. The main difficulty
lies in the acquisition of a representative data set of cell images which ideally contain various sizes, shapes and distributions
for a variety of cell types. Additionally, the manual annotation of the acquired data is mandatory to obtain
the so called ‘ground truth’ or ‘labels’, which is in general error-prone, time-consuming and costly to obtain.
Differentiable rendering [11–13] on the other hand is a emerging technique, which allows to generate synthetic,
photo-realistic images based on photographs of real-world objects by estimating its 3D shape and material properties.
While this approach can be used to generate photo-realistic images, it can also be applied for the generation of
respective ground truth labels for segmentation and object detection masks. Combining differentiable rendering with
deep learning could potentially solve the data bottleneck for machine learning algorithms in various fields, including
materials science and biomedical engineering.
The work of this thesis is based on the differentiable rendering framework ‘Redner’ [11] using data from the Cell
Tracking Challenge [14, 15]. In a first step, a literature research will be conducted on the topic of differentiable rendering.
In a second step, an existing implementation for the light, shader and geometry estimation of nanoparticles
will be adapted for the semi-supervised segmentation of GFP-GOWT1 mouse stem cells. Afterwards, the results of
this approach will be evaluated in terms of segmentation accuracy.
The thesis will include the following points:
• Getting familiar with the concepts of Differentiable Rendering and Gradient-based learning methods
• Implementation of a proof-of-concept for the semi-supervised segmentation of cells based on the ‘Redner’
framework using existing data from the Cell Tracking Challenge
• Evaluation of the method in terms of segmentation accuracy
• Elaboration of potential improvements for the method
Academic advisors:

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