Federated Multi-Organ Segmentation With Inconsistent Labels.

Federated learning is an emerging paradigm allowing large-scale decentralized learning without sharing data across different data owners, which helps address the concern of data privacy in medical image analysis. However, the requirement for label consistency across clients by the existing methods largely narrows its application scope. In practice, each clinical site may only annotate certain organs of interest with partial or no overlap with other sites. Incorporating such partially labeled data into a unified federation is an unexplored problem with clinical significance and urgency. This work tackles the challenge by using a novel federated multi-encoding U-Net (Fed-MENU) method for multi-organ segmentation. In our method, a multi-encoding U-Net (MENU-Net) is proposed to extract organ-specific features through different encoding sub-networks. Each sub-network can be seen as an expert of a specific organ and trained for that client. Moreover, to encourage the organ-specific features extracted by different sub-networks to be informative and distinctive, we regularize the training of the MENU-Net by designing an auxiliary generic decoder (AGD). Extensive experiments on six public abdominal CT datasets show that our Fed-MENU method can effectively obtain a federated learning model using the partially labeled datasets with superior performance to other models trained by either localized or centralized learning methods. Source code is publicly available at https://github.com/DIAL-RPI/Fed-MENU.

Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning.

PURPOSE: Orthognathic surgery requires an accurate surgical plan of how bony segments are moved and how the face passively responds to the bony movement. Currently, finite element method (FEM) is the standard for predicting facial deformation. Deep learning models have recently been used to approximate FEM because of their faster simulation speed. However, current solutions are not compatible with detailed facial meshes and often do not explicitly provide the network with known boundary type information. Therefore, the purpose of this proof-of-concept study is to develop a biomechanics-informed deep neural network that accepts point cloud data and explicit boundary types as inputs to the network for fast prediction of soft-tissue deformation. METHODS: A deep learning network was developed based on the PointNet++ architecture. The network accepts the starting facial mesh, input displacement, and explicit boundary type information and predicts the final facial mesh deformation. RESULTS: We trained and tested our deep learning model on datasets created from FEM simulations of facial meshes. Our model achieved a mean error between 0.159 and 0.642 mm on five subjects. Including explicit boundary types had mixed results, improving performance in simulations with large deformations but decreasing performance in simulations with small deformations. These results suggest that including explicit boundary types may not be necessary to improve network performance. CONCLUSION: Our deep learning method can approximate FEM for facial change prediction in orthognathic surgical planning by accepting geometrically detailed meshes and explicit boundary types while significantly reducing simulation time.

A comparison between landmark and surface shape measurements in a sample of cleft lip and palate patients after secondary alveolar bone grafting.

AIM: To compare landmark vs surface-shape measurements in a sample of patients with cleft lips and palates following secondary alveolar bone grafting. METHODS: The faces of 10 patients (4 males and 6 females) with an unilateral cleft lip and palate were captured using a 3D surface camera system before and 6 weeks after alveolar bone grafting. In each face, six coordinates were registered. The pre- and postoperative images were superimposed on areas that were not affected by the surgery. Using 3D modeling software landmarks, nasal symmetry, and surface-to-surface deviation, analysis was performed. All data were subjected to standard statistical analyses. RESULTS: Color map surface-to-surface comparison revealed a significant anteroposterior elevation in the nasal region of the cleft side after surgery. CONCLUSION: The ala, alar base, and paranasal areas are increased anteroposteriorly after secondary bone grafting. This surgery tends to diminish the asymmetry in nasal morphology typically seen in patients with unilateral cleft lip and palate. Overall, 3D surface-to-surface analysis allows for a better quantification of treatment changes.