Partner: H. Skibbe


Ostatnie publikacje
1.Byra M., Poon C., Rachmadi Muhammad F., Schlachter M., Skibbe H., Exploring the performance of implicit neural representations for brain image registration, Scientific Reports, ISSN: 2045-2322, DOI: 10.1038/s41598-023-44517-5, Vol.13, No.17334, pp.1-13, 2023

Streszczenie:

Pairwise image registration is a necessary prerequisite for brain image comparison and data integration in neuroscience and radiology. In this work, we explore the efficacy of implicit neural representations (INRs) in improving the performance of brain image registration in magnetic resonance imaging. In this setting, INRs serve as a continuous and coordinate based approximation of the deformation field obtained through a multi-layer perceptron. Previous research has demonstrated that sinusoidal representation networks (SIRENs) surpass ReLU models in performance. In this study, we first broaden the range of activation functions to further investigate the registration performance of implicit networks equipped with activation functions that exhibit diverse oscillatory properties. Specifically, in addition to the SIRENs and ReLU, we evaluate activation functions based on snake, sine+, chirp and Morlet wavelet functions. Second, we conduct experiments to relate the hyper-parameters of the models to registration performance. Third, we propose and assess various techniques, including cycle consistency loss, ensembles and cascades of implicit networks, as well as a combined image fusion and registration objective, to enhance the performance of implicit registration networks beyond the standard approach. The investigated implicit methods are compared to the VoxelMorph convolutional neural network and to the symmetric image normalization (SyN) registration algorithm from the Advanced Normalization Tools (ANTs). Our findings not only highlight the remarkable capabilities of implicit networks in addressing pairwise image registration challenges, but also showcase their potential as a powerful and versatile off-the-shelf tool in the fields of neuroscience and radiology.

Afiliacje autorów:

Byra M.-IPPT PAN
Poon C.-other affiliation
Rachmadi Muhammad F.-other affiliation
Schlachter M.-other affiliation
Skibbe H.-other affiliation
140p.

Prace konferencyjne
1.Poon Ch., Rachmadi M.F., Byra M., Schlachter M., Xu B., Shimogori T., Skibbe H., AN AUTOMATED PIPELINE TO CREATE AN ATLAS OF IN SITU HYBRIDIZATION GENE EXPRESSION DATA IN THE ADULT MARMOSET BRAIN, ISBI, 2023 IEEE 20th International Symposium on Biomedical Imaging, 2023-04-18/04-21, Cartagena (CO), DOI: 10.1109/ISBI53787.2023.10230544, pp.1-5, 2023

Streszczenie:

We present the first automated pipeline to create an atlas of in situ hybridization gene expression in the adult marmoset brain in the same stereotaxic space. The pipeline consists of segmentation of gene expression from microscopy images and registration of images to a standard space. Automation of this pipeline is necessary to analyze the large volume of data in the genome-wide whole-brain dataset, and to process images that have varying intensity profiles and expression patterns with minimal human bias. To reduce the number of labelled images required for training, we develop a semi-supervised segmentation model. We further develop an iterative algorithm to register images to a standard space, enabling comparative analysis between genes and concurrent visualization with other datasets, thereby facilitating a more holistic understanding of primate brain structure and function.

Słowa kluczowe:

contrastive learning, gene atlas, segmen-tation, semi-supervised learning, registration

Afiliacje autorów:

Poon Ch.-other affiliation
Rachmadi M.F.-other affiliation
Byra M.-IPPT PAN
Schlachter M.-other affiliation
Xu B.-Tsinghua University (CN)
Shimogori T.-other affiliation
Skibbe H.-other affiliation
2.Byra M., Poon Ch., Shimogori T., Skibbe H., Implicit Neural Representations for Joint Decomposition and Registration of Gene Expression Images in the Marmoset Brain, MICCAI 2023, Medical Image Computing and Computer-Assisted Intervention, 2023-10-08/10-12, Vancouver (CA), DOI: 10.48550/arXiv.2308.04039, pp.1, 2023

Streszczenie:

We propose a novel image registration method based on implicit neural representations that addresses the challenging problem of registering a pair of brain images with similar anatomical structures, but where one image contains additional features or artifacts that are not present in the other image. To demonstrate its effectiveness, we use 2D microscopy in situ hybridization gene expression images of the marmoset brain. Accurately quantifying gene expression requires image registration to a brain template, which is difficult due to the diversity of patterns causing variations in visible anatomical brain structures. Our approach uses implicit networks in combination with an image exclusion loss to jointly perform the registration and decompose the image into a support and residual image. The support image aligns well with the template, while the residual image captures individual image characteristics that diverge from the template. In experiments, our method provided excellent results and outperformed other registration techniques.

Słowa kluczowe:

brain, deep learning, gene expression, implicit neural representations, registration

Afiliacje autorów:

Byra M.-IPPT PAN
Poon Ch.-other affiliation
Shimogori T.-other affiliation
Skibbe H.-other affiliation

Abstrakty konferencyjne
1.Poon Ch., Rachmadi M.F., Byra M., Shimogori T., Skibbe H., Semi-supervised contrastive learning for semantic segmentation of ISH gene expression in the marmoset brain, NEURO2022, The 45th Annual Meeting of the Japan Neuroscience Society The 65th Annual Meeting of the Japanese Society for Neurochemistry The 32nd Annual Conference of the Japanese Neural Network Society, 2022-06-30/07-03, Okinawa (JP), pp.1, 2022