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IPMI 2021 poster

TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer

Authors: Fan Wang (Stony Brook University)*; Saarthak Kapse (Indian Institute of Technology Bombay); Steven H. Liu (Stony Brook University); Prateek Prasanna (Stony Brook University); Chao Chen (Stony Brook University)

Poster

Abstract: Characterization of breast parenchyma on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Current quantitative approaches, including radiomics and deep learning models, do not explicitly capture the complex and subtle parenchymal structures, such as fibroglandular tissue. In this paper, we propose a novel method to direct a neural network’s attention to a dedicated set of voxels surrounding geometrically relevant tissue structures. By extracting multi-dimensional topological structures with high saliency, we build a topology-derived biomarker, TopoTxR. We demonstrate the efficacy of TopoTxR in predicting response to neoadjuvant chemotherapy in breast cancer. Our qualitative and quantitative results suggest differential topological behavior, on treatment-naïve imaging, in patients who respond favorably to therapy versus those who do not.

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IPMI 2021 poster

Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders

Authors: Xin Ma (The University of Texas at Arlington)*; Guorong Wu (University of North Carolina); Seong Jae Hwang (University of Pittsburgh); Won Hwa Kim (POSTECH)

Poster

Abstract: Tremendous recent literature show that associations between different brain regions, i.e., brain connectivity, provide early symptoms of neurological disorders. Despite significant efforts made for graph neural network (GNN) techniques, their focus on graph nodes makes the state-of-the-art GNN methods not suitable for classifying brain connectivity as graphs where the objective is to characterize disease-relevant network dysfunction patterns on graph links. To address this issue, we propose Multi-resolution Edge Network (MENET) to detect disease-specific connectomic benchmarks with high discrimination power across diagnostic categories. The core of MENET is a novel graph edge-wise transform that we propose, which allows us to capture multi-resolution “connectomic” features. Using a rich set of the connectomic features, we devise a graph learning framework to jointly select discriminative edges and assign diagnostic labels for graphs. Experiments on two real datasets show that MENET accurately predicts diagnostic labels and identify brain connectivities highly associated with neurological disorders such as Alzheimer’s Disease and Attention-Deficit/Hyperactivity Disorder.

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IPMI 2021 oral

Cytoarchitecture Measurements in Brain Gray Matter using Likelihood-Free Inference

Authors: Maëliss Jallais (Inria)*; Pedro Rodrigues (Inria); Alexandre Gramfort (Inria); Demian Wassermann (Inria)

Poster

Abstract: Effective characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in diffusion MRI (dMRI). Solving the problem of relating the dMRI signal with cytoarchitectural characteristics calls for the definition of a mathematical model that describes brain tissue via a handful of physiologically-relevant parameters and an algorithm for inverting the model. To address this issue, we propose a new forward model, specifically a new system of equations, requiring six relatively sparse b-shells. These requirements are a drastic reduction of those used in current proposals to estimate grey matter cytoarchitecture. We then apply current tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model. As opposed to other approaches from the literature, our LFI-based algorithm yields not only an estimation of the parameter vector that best describes a given observed data point, but also a full posterior distribution over the parameter space. This enables a richer description of the model inversion results providing indicators such as confidence intervals for the estimations, and better understanding of the parameter regions where the model may present indeterminacies. We approximate the posterior distribution using deep neural density estimators, known as normalizing flows, and fit them using a set of repeated simulations from the forward model. We validate our approach on simulations using dmipy and then apply the whole pipeline to the HCP MGH dataset.

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IPMI 2021 poster

Blind stain separation using model-aware generative learning and its applications on uorescence microscopy images

Authors: Xingyu Li (University of Alberta)*

Poster

Abstract: Multiple stains are usually used to highlight biological substances in biomedical image analysis. To decompose multiple stains for co-localization quantification, blind source separation is usually performed. Prior model-based stain separation methods usually rely on stains’ spatial distributions over an image and may fail to solve the co-localization problem. With the advantage of machine learning, deep generative models are used for this purpose. Since prior knowledge of imaging models is ignored in purely data-driven solutions, these methods may be sub-optimal. In this study, a novel learning-based blind source separation framework is proposed, where the physical model of biomedical imaging is incorporated to regularize the learning process. The introduced model-relevant adversarial loss couples all generators in the framework and limits the capacities of the generative models. Further more, a training algorithm is innovated for the proposed framework to avoid inter-generator confusion during learning. This paper particularly takes fluorescence unmixing in fluorescence microscopy images as an application example of the proposed framework. Qualitative and quantitative experimentation on a public fluorescence microscopy image set demonstrates the superiority of the proposed method over both prior model-based approaches and learning-based methods.

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IPMI 2021 poster

Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation

Authors: Hong-Yu Zhou (The University of Hong Kong)*; Hualuo Liu (Jilin University); Shilei Cao (Tencent); DONG WEI (Tencent Jarvis Lab); Chixiang Lu (Huazhong University of Science and Technology); Yizhou Yu (The University of Hong Kong); Kai Ma (Tencent); Yefeng Zheng (Tencent)

Poster

Abstract: Learning by imitation is one of the most significant abilities of human beings and plays a vital role in human’s computational neural system. In medical image analysis, given several exemplars (anchors), experienced radiologist has the ability to delineate unfamiliar organs by imitating the reasoning process learned from existing types of organs. Inspired by this observation, we propose \emph{OrganNet} which learns a generalized organ concept from a set of annotated organ classes and then transfer this concept to unseen classes. In this paper, we show that such process can be integrated into the one-shot segmentation task which is a very challenging but meaningful topic. We propose pyramid reasoning modules (PRMs) to model the anatomical correlation between anchor and target volumes. In practice, the proposed module first computes a correlation matrix between target and anchor computerized tomography (CT) volumes. Then, this matrix is used to transform the feature representations of both anchor volume and its segmentation mask. Finally, OrganNet learns to fuse the representations from various inputs and predicts segmentation results for target volume. Extensive experiments show that OrganNet can effectively resist the wide variations in organ morphology and produce state-of-the-art results in one-shot segmentation task. Moreover, even when compared with fully-supervised segmentation models, OrganNet is still able to produce satisfying segmentation results.

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IPMI 2021 poster

Is segmentation uncertainty useful?

Authors: Steffen Czolbe (University of Copenhagen)*; Kasra Arnavaz (University of Copenhagen); Oswin Krause (University of Copenhagen); Aasa Feragen (Technical University of Denmark)

Poster

Abstract: Probabilistic image segmentation encodes varying prediction confidence and inherent ambiguity in the segmentation problem. While different probabilistic segmentation models are designed to capture different aspects of segmentation uncertainty and ambiguity, these modelling differences are rarely discussed in the context of applications of uncertainty.
We consider two common use cases of segmentation uncertainty, namely assessment of segmentation quality and active learning. We consider four established strategies for probabilistic segmentation, discuss their modelling capabilities, and investigate their performance in these two tasks. We find that for all models and both tasks, returned uncertainty correlates positively with segmentation error, but does not prove to be useful for active learning.

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IPMI 2021 oral

Partial Matching in the Space of Varifolds

Authors: Pierre-Louis A Antonsanti (GE Healthcare)*; Joan Glaunès (Université Paris 5); Thomas Benseghir (GE Healthcare); Vincent Jugnon (GE Healthcare); Irene Kaltenmark (MAP5 – Université de Paris)

Poster

Abstract:

In computer vision and medical imaging, the problem of matching structures finds numerous applications from automatic annotation to data reconstruction. The data however, while corresponding to the same anatomy, are often very different in topology or shape and might only partially match each other.
We introduce a new asymmetric data dissimilarity term for geometric shapes like curves, sets of curves or surfaces. This term is based on the Varifold shape representation and assess the embedding of a shape into another without relying on correspondences between points. It is designed as data attachment for the Large Deformation Diffeomorphic Metric Mapping (LDDMM), allowing to compute meaningful deformation of one shape onto a subset of the other. Registrations are illustrated on sets of synthetic 3D curves, real vascular trees and livers’ surfaces from two different modalities: Computed Tomography (CT) and Cone Beam Computed Tomography (CBCT). All experiments show that these data dissimilarity terms lead to coherent partial matching despite the topological differences.

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IPMI 2021 oral

Velocity-To-Pressure (V2P) – Net: Inferring Relative Pressures from Time-Varying 3D Fluid Flow Velocities

Authors: Suprosanna Shit (TUM)*; Dhritiman Das (Massachusetts Institute of Technology ); Ivan Ezhov (TUM); Johannes C. Paetzold (TUM); Augusto F Fava Sanches (TUM); Nils Thuerey (Technical University of Munich); Bjoern Menze (TUM)

Poster

Abstract: Pressure inference from a series of velocity fields is a common problem arising in medical imaging when analyzing 4D data. Traditional approaches primarily rely on a numerical scheme to solve the pressure-Poisson equation to obtain a dense pressure inference. This involves heavy expert intervention at each stage and requires significant computational resources. Concurrently, the application of current machine learning algorithms for solving partial differential equations is limited to domains with simple boundary conditions. We address these challenges in this paper and present V2P-Net: a novel, neural-network-based approach as an alternative method for inferring pressure from the observed velocity fields. We design an end-to-end hybrid-network architecture motivated by the conventional Navier-Stokes solver, which encapsulates the complex boundary conditions. It achieves accurate pressure estimation compared to the reference numerical solver for simulated flow data in multiple complex geometries of human in-vivo vessels.

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IPMI 2021 poster

EnMcGAN: Adversarial Ensemble Learning for 3D Complete Renal Structures Segmentation

Authors: Yuting He (Southeast University); Rongjun Ge (Southeast University); Xiaoming Qi (Southeast University); Guanyu Yang (Southeast University)*; Yang Chen (Southeast University); Youyong Kong (Southeast University); Huazhong Shu (Southeast University); Jean-Louis Coatrieux (” LTSI, Rennes, France”); Shuo Li (the University of Western Ontario)

Poster

Abstract: 3D complete renal structures (CRS) segmentation targets on segmenting the kidneys, tumors, renal arteries and veins in one inference. Once successful, it will provide preoperative plans and intraoperative guidance for laparoscopic partial nephrectomy (LPN), playing a key role in the renal cancer treatment. However, no success has been reported in 3D CRS segmentation due to the complex shapes of renal structures, low contrast and large anatomical variation. In this study, we utilize the adversarial ensemble learning and propose Ensemble Multi-condition GAN (EnMcGAN) for 3D CRS segmentation for the first time. Its contribution is three-fold. 1) Inspired by windowing, we propose the multi-windowing committee which divides CTA image into multiple narrow windows with different window centers and widths enhancing the contrast for salient boundaries and soft tissues. And then, it builds an ensemble segmentation model on these narrow windows to fuse the segmentation superiorities and improve whole segmentation quality. 2) We propose the multi-condition GAN which equips the segmentation model with multiple discriminators to encourage the segmented structures meeting their real shape conditions, thus improving the shape feature extraction ability. 3) We propose the adversarial weighted ensemble module which uses the trained discriminators to evaluate the quality of segmented structures, and normalizes these evaluation scores for the ensemble weights directed at the input image, thus enhancing the ensemble results. 122 patients are enrolled in this study and the mean Dice coefficient of the renal structures achieves 84.6%. Extensive experiments with promising results on renal structures reveal powerful segmentation accuracy and great clinical significance in renal cancer treatment.

Categories
IPMI 2021 oral

Deep learning based geometric registration for medical images: How accurate can we get without visual features?

Authors: Lasse Hansen (University of Luebeck)*; Mattias Heinrich (University of Luebeck)

Poster

Abstract: As in other areas of medical image analysis, e.g. semantic segmentation, deep learning is currently driving the development of new approaches for image registration. Multi-scale encoder-decoder network architectures achieve state-of-the-art accuracy on tasks such as intra-patient alignment of abdominal CT or brain MRI registration, especially when additional supervision, such as anatomical labels, is available. The success of these methods relies to a large extent on the outstanding ability of deep CNNs to extract descriptive visual features from the input images. In contrast to conventional methods, the explicit inclusion of geometric information plays only a minor role, if at all. In this work we take a look at an exactly opposite approach by investigating a deep learning framework for registration based solely on geometric features and optimisation. We combine graph convolutions with loopy belief message passing to enable highly accurate 3D point cloud registration. Our experimental validation is conducted on complex key-point graphs of inner lung structures, strongly outperforming dense encoder-decoder networks and other point set registration methods. Our code is publicly available at https://github.com/multimodallearning/deep-geo-reg.