Latent Disentanglement in Mesh Variational Autoencoders Improves the Diagnosis of Craniofacial Syndromes and Aids Surgical Planning

• URL: http://arxiv.org/abs/2309.10825v1
• Date: Tue, 5 Sep 2023 13:16:53 GMT
• Title: Latent Disentanglement in Mesh Variational Autoencoders Improves the Diagnosis of Craniofacial Syndromes and Aids Surgical Planning
• Authors: Simone Foti, Alexander J. Rickart, Bongjin Koo, Eimear O' Sullivan, Lara S. van de Lande, Athanasios Papaioannou, Roman Khonsari, Danail Stoyanov, N. u. Owase Jeelani, Silvia Schievano, David J. Dunaway, Matthew J. Clarkson
• Abstract summary: We will discuss the application of the Swap Disentangled Variational Autoencoder with relevance to Crouzon, Apert and Muenke syndromes. By manipulating specific parameters of the generative model, it is also possible to simulate the outcome of a range of craniofacial surgical procedures.
• Score: 42.017495658167334
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise. However, there have traditionally been a number of barriers to accurate modelling, especially when operating on both a global and local level. In this work, we will discuss the application of the Swap Disentangled Variational Autoencoder (SD-VAE) with relevance to Crouzon, Apert and Muenke syndromes. Although syndrome classification is performed on the entire mesh, it is also possible, for the first time, to analyse the influence of each region of the head on the syndromic phenotype. By manipulating specific parameters of the generative model, and producing procedure-specific new shapes, it is also possible to simulate the outcome of a range of craniofacial surgical procedures. This opens new avenues to advance diagnosis, aids surgical planning and allows for the objective evaluation of surgical outcomes.

Related papers

• Fast Medical Shape Reconstruction via Meta-learned Implicit Neural Representations [5.213304732451705]
  Minimizing retrieval and processing times potentially enhances swift response and decision-making in critical scenarios. Recent methods attempt to solve the medical shape reconstruction problem by utilizing implicit neural functions.
  arXiv  Detail & Related papers  (2024-09-11T08:44:10Z)
• AG-CRC: Anatomy-Guided Colorectal Cancer Segmentation in CT with Imperfect Anatomical Knowledge [9.961742312147674]
  We develop a novel Anatomy-Guided segmentation framework to exploit the auto-generated organ masks. We extensively evaluate the proposed method on two CRC segmentation datasets.
  arXiv  Detail & Related papers  (2023-10-07T03:22:06Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles [52.77024349608834]
  We analyze the influence of replacing a DCNN with a state-of-the-art face recognition approach, iResNet with ArcFace. Our proposed ensemble model achieves state-of-the-art performance on both seen and unseen disorders.
  arXiv  Detail & Related papers  (2022-11-12T23:28:54Z)
• Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation [70.7778938191405]
  We propose a novel factored attention and embedding model (termed FAE-Gen) for the unstructured-view topic-related ultrasound report generation. The proposed FAE-Gen mainly consists of two modules, i.e., view-guided factored attention and topic-oriented factored embedding, which capture the homogeneous and heterogeneous morphological characteristic across different views.
  arXiv  Detail & Related papers  (2022-03-12T15:24:03Z)
• Explainable multiple abnormality classification of chest CT volumes with AxialNet and HiResCAM [89.2175350956813]
  We introduce the challenging new task of explainable multiple abnormality classification in volumetric medical images. We propose a multiple instance learning convolutional neural network, AxialNet, that allows identification of top slices for each abnormality. We then aim to improve the model's learning through a novel mask loss that leverages HiResCAM and 3D allowed regions.
  arXiv  Detail & Related papers  (2021-11-24T01:14:33Z)
• A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction [1.41414531071294]
  The article aims to survey the advancement of automated methods for Glioma brain tumor segmentation. It is also essential to make an objective evaluation of various models based on the benchmark. The paper covers the complete gamut of brain tumor segmentation using handcrafted features to deep neural network models for Task 1.
  arXiv  Detail & Related papers  (2021-01-26T07:22:52Z)
• Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting [4.019851137611981]
  This paper proposes a fully automatic, unsupervised inpainting-based brain tumour segmentation system for T1-weighted MRI. First, a deep convolutional neural network (DCNN) is trained to reconstruct missing healthy brain regions. Then, anomalous regions are determined by identifying areas of highest reconstruction loss. We show the proposed system is able to segment various sized and abstract tumours and achieves a mean and standard deviation Dice score of 0.771 and 0.176, respectively.
  arXiv  Detail & Related papers  (2020-10-05T12:13:44Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.