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:

References

[1] Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

[2] K. He, G. Gkioxari, P. Dollar, and R. Girshick, “Mask R-CNN,” Proceedings of the IEEE International Conference

on Computer Vision, vol. 2017-Octob, pp. 2980–2988, 2017.

[3] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv

preprint arXiv:1409.1556, 2014.

[4] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE

conference on computer vision and pattern recognition, pp. 770–778, 2016.

[5] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,”

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-

Decem, pp. 779–788, 2016.

[6] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,”

in International Conference on Medical image computing and computer-assisted intervention, pp. 234–241,

Springer, 2015.

[7] T. Falk, D. Mai, R. Bensch, O¨ . C¸ ic¸ek, A. Abdulkadir, Y. Marrakchi, A. Bo¨hm, J. Deubner, Z. Ja¨ckel, K. Seiwald,

A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons,

I. Diester, T. Brox, and O. Ronneberger, “U-Net: deep learning for cell counting, detection, and morphometry,”

Nature Methods, vol. 16, no. 1, pp. 67–70, 2019.

[8] Jia Deng, Wei Dong, R. Socher, Li-Jia Li, Kai Li, and Li Fei-Fei, “ImageNet: A large-scale hierarchical image

database,” 2009 IEEE Conference on Computer Vision and Pattern Recognition, no. May 2014, pp. 248–255,

2009.

[9] D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou,

V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap,

M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks

and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, 2016.

[10] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,”

in Advances in neural information processing systems, pp. 1097–1105, 2012.

[11] T.-M. Li, M. Aittala, F. Durand, and J. Lehtinen, “Differentiable monte carlo ray tracing through edge sampling,”

ACM Trans. Graph., vol. 37, Dec. 2018.

[12] M. Nimier-David, D. Vicini, T. Zeltner, and W. Jakob, “Mitsuba 2: a retargetable forward and inverse renderer,”

ACM Transactions on Graphics (TOG), vol. 38, no. 6, p. 203, 2019.

[13] G. Loubet, N. Holzschuch, andW. Jakob, “Reparameterizing discontinuous integrands for differentiable rendering,”

ACM Transactions on Graphics (TOG), vol. 38, no. 6, pp. 1–14, 2019.

[14] M. Maˇska, V. Ulman, D. Svoboda, P. Matula, P. Matula, C. Ederra, A. Urbiola, T. Espa˜na, S. Venkatesan,

D. M. Balak, et al., “A benchmark for comparison of cell tracking algorithms,” Bioinformatics, vol. 30, no. 11,

pp. 1609–1617, 2014.

[15] V. Ulman, M. Maˇska, K. E. Magnusson, O. Ronneberger, C. Haubold, N. Harder, P. Matula, P. Matula, D. Svoboda,

M. Radojevic, et al., “An objective comparison of cell-tracking algorithms,” Nature methods, vol. 14,

no. 12, p. 1141, 2017.