Dataset Information

A Growth-Based Framework for Leaf Shape Development and Diversity.

ABSTRACT: How do genes modify cellular growth to create morphological diversity? We study this problem in two related plants with differently shaped leaves: Arabidopsis thaliana (simple leaf shape) and Cardamine hirsuta (complex shape with leaflets). We use live imaging, modeling, and genetics to deconstruct these organ-level differences into their cell-level constituents: growth amount, direction, and differentiation. We show that leaf shape depends on the interplay of two growth modes: a conserved organ-wide growth mode that reflects differentiation; and a local, directional mode that involves the patterning of growth foci along the leaf edge. Shape diversity results from the distinct effects of two homeobox genes on these growth modes: SHOOTMERISTEMLESS broadens organ-wide growth relative to edge-patterning, enabling leaflet emergence, while REDUCED COMPLEXITY inhibits growth locally around emerging leaflets, accentuating shape differences created by patterning. We demonstrate the predictivity of our findings by reconstructing key features of C. hirsuta leaf morphology in A. thaliana. VIDEO ABSTRACT.

SUBMITTER: Kierzkowski D

PROVIDER: S-EPMC6548024 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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A Growth-Based Framework for Leaf Shape Development and Diversity.

Kierzkowski Daniel D Runions Adam A Vuolo Francesco F Strauss Sören S Lymbouridou Rena R Routier-Kierzkowska Anne-Lise AL Wilson-Sánchez David D Jenke Hannah H Galinha Carla C Mosca Gabriella G Zhang Zhongjuan Z Canales Claudia C Dello Ioio Raffaele R Huijser Peter P Smith Richard S RS Tsiantis Miltos M

Cell 20190523 6

How do genes modify cellular growth to create morphological diversity? We study this problem in two related plants with differently shaped leaves: Arabidopsis thaliana (simple leaf shape) and Cardamine hirsuta (complex shape with leaflets). We use live imaging, modeling, and genetics to deconstruct these organ-level differences into their cell-level constituents: growth amount, direction, and differentiation. We show that leaf shape depends on the interplay of two growth modes: a conserved organ ...[more]

PMID: 31130379

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Project description:To improve the accuracy and the robustness of the segmentation in living donor liver transplantation (LDLT) surgery planning system, the authors present a new segmentation framework that addresses challenges induced by the complex shape variations of patients' livers with cancer. It is designed to achieve the accurate and robust segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors in the LDLT surgery planning system.The segmentation framework proposed in this paper includes two important modules: (1) The robust shape prior modeling for liver, in which the sparse shape composition (SSC) model is employed to deal with the complex variations of liver shapes and obtain patient-specific liver shape priors. (2) The integration of the liver shape prior with a minimally supervised segmentation algorithm to achieve the accurate segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors simultaneously. The authors apply this segmentation framework to our previously developed LDLT surgery planning system to enhance its accuracy and robustness when dealing with complex cases of patients with liver cancer.Compared with the principal component analysis, the SSC model shows a great advantage in handling the complex variations of liver shapes. It also effectively excludes gross errors and outliers that appear in the input shape and preserves local details for specific patients. The proposed segmentation framework was evaluated on the clinical image data of liver cancer patients, and the average symmetric surface distance for hepatic parenchyma, portal veins, hepatic veins, and tumors was 1.07 ± 0.76, 1.09 ± 0.28, 0.92 ± 0.35 and 1.13 ± 0.37 mm, respectively. The Hausdorff distance for these four tissues was 7.68, 4.67, 4.09, and 5.36 mm, respectively.The proposed segmentation framework improves the robustness of the LDLT surgery planning system remarkably when dealing with complex clinical liver shapes. The SSC model is able to handle non-Gaussian errors and preserve local detail information of the input liver shape. As a result, the proposed framework effectively addresses the problems caused by the complex shape variations of livers with cancer. Our framework not only obtains accurate segmentation results for healthy persons and common patients, but also shows high robustness when dealing with specific patients with large variations of liver shapes in complex clinical environments.

Dataset Information

A Growth-Based Framework for Leaf Shape Development and Diversity.

Publications

A Growth-Based Framework for Leaf Shape Development and Diversity.

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