Construction of Shape Atlas for Abdominal Organs using Three-Dimensional Mesh Variational Autoencoder

2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 24-27 July 2023, Sydney, AustraliaA model that represents the shapes and positions of organs or skeletal structures with a small number of parameters may be expected to have a wide range of clinical applications, such as radiotherapy and surgical guidance. However, because soft organs vary in shape and position between patients, it is difficult for linear models to reconstruct locally variable shapes, and nonlinear models are prone to overfitting, particularly when the quantity of data is small. The aim of this study was to construct a shape atlas with high accuracy and good generalization performance. We designed a mesh variational autoencoder that can reconstruct both nonlinear shape and position with high accuracy. We validated the trained model for liver meshes of 125 cases, and found that it was possible to reconstruct the positions and shapes with an average accuracy of 4.3 mm for the test data of 19 cases

Role of membrane sphingomyelin and ceramide in platform formation for Fas-mediated apoptosis

Engagement of the Fas receptor (CD95) initiates multiple signaling pathways that lead to apoptosis, such as the formation of death-inducing signaling complex (DISC), activation of caspase cascades, and the generation of the lipid messenger, ceramide. Sphingomyelin (SM) is a major component of lipid rafts, which are specialized structures that enhance the efficiency of membrane receptor signaling and are a main source of ceramide. However, the functions of SM in Fas-mediated apoptosis have yet to be clearly defined, as the responsible genes have not been identified. After cloning a gene responsible for SM synthesis, SMS1, we established SM synthase–defective WR19L cells transfected with the human Fas gene (WR/Fas-SM(−)), and cells that have been functionally restored by transfection with SMS1 (WR/Fas-SMS1). We show that expression of membrane SM enhances Fas-mediated apoptosis through increasing DISC formation, activation of caspases, efficient translocation of Fas into lipid rafts, and subsequent Fas clustering. Furthermore, WR/Fas-SMS1 cells, but not WR/Fas-SM(−) cells, showed a considerable increase in ceramide generation within lipid rafts upon Fas stimulation. These data suggest that a membrane SM is important for Fas clustering through aggregation of lipid rafts, leading to Fas-mediated apoptosis

3次元メッシュ変分オートエンコーダーを用いた臓器形状アトラスの構築

生体が有する臓器や骨格の形状と位置を少ないパラメーターで表現するモデルは放射線治療や外科手術支援等など幅広い臨床応用が期待できる。しかし、軟臓器は患者間で多様な形状と位置を取るため、線形モデルでは局所的に変化が大きい形状を再構成することは難しく、非線形モデルでは特にデータ数が少ないときに過学習に陥りやすい。本研究では高精度で汎化性能が高い形状モデルの構築を目指し、3 次元メッシュデータを入出力として、形状だけではなく位置も含めて高精度に再構成可能なメッシュ変分オートエンコーダーを構築した。125例からなる肝臓の臓器形状メッシュデータを用いて学習したモデルの検証を行い、内19症例のテストデータに対して、平均4.3mmの精度で位置と形状の再構成が可能であることを確認したので報告する。A model that represents the shapes and positions of organ or skeletal structures with a small number of parameters may be expected to have a wide range of clinical applications, such as radiotherapy and surgical guidance. However, because soft organs vary in shape and position between patients, it is difficult for linear models to reconstruct locally variable shapes, and nonlinear models are prone to overfitting, particularly when the quantity of data is small. The aim of this study was to construct a shape atlas with high accuracy and good generalization performance. We designed a mesh variational autoencoder that can reconstruct both nonlinear shape and position with high accuracy. We validated the trained model for liver meshes of 125 cases, and found that it was possible to reconstruct the positions and shapes with an average accuracy of 4.3 mm for the test data of 19 cases

階層的潜在変数を用いたMeshVAEによる臓器形状アトラスの構築

生体が有する臓器や骨格の形状と位置を少ないパラメータで表現するモデルは幅広い臨床応用が期待される．しかし，軟臓器の形状と位置は患者間で大きく異なるため，線形モデルでは局所的に変化が大きい形状を再構成することは難しく，従来の非線形モデルでは得られた臓器形状の制御や解釈が難しい．そこで本研究では Mesh Variational AutoEncoder に階的潜在変数を導入した臓器形状アトラスを提案する．提案手法は潜在変数を階層化することによって，患者間の形状差の潜在変数を階層別に分析することを可能とし，非線形な臓器形状表現の解釈性を向上させることが可能である．124例からなる肝臓の臓器メッシュデータを対象に構築したモデルの検証を行い，内19症例のテストデータに対して，平均1.4mmの頂点間距離，0.8mm の平均距離で位置と形状の再構成が可能であることを確認した．また，異なる階層の潜在変数を変更することで階層ごとに表現される形状が異なることを確認したので報告する．Abstract Models that represent the shape and position of the organs and skeleton possessed by a living body with few parameters are expected to have a wide range of clinical applications. However, since the shape and position of soft organs vary greatly from patient to patient, it is difficult for linear models to reconstruct shapes with large local variations, and it is difficult to control and interpret the obtained organ shapes with conventional nonlinear models. Therefore, we propose an organ shape atlas that introduces hierarchical latent variables into Mesh Variational AutoEncoder. The proposed method can improve the interpretability of nonlinear organ shape representation by introducing hierarchical latent variables. We confirmed that position and shape reconstruction was possible with an average vertex-to-vertex distance of 1.4 mm and a mean distance of 0.8 mm for the data. We also report that we confirmed that the shapes represented in each hierarchy were different by morphing