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IEEE TRANSACTIONS ON MEDICAL IMAGING 2009年第4期28卷 571-584页

作者： Liu, Jiamin Udupa, Jayaram K. Univ Penn Dept Radiol Med Image Proc Grp Philadelphia PA 19104 USA NIH Virtual Endoscopy & Comp Aided Diag Lab Dept Radiol & Imaging Sci Bethesda MD 20892 USA

Active shape models (ASM) are widely employed for recognizing anatomic structures and for delineating them in medical images. In this paper, a novel strategy called oriented active shape models (OASM) is presented in an attempt to overcome the following five limitations of ASM: 1) lower delineation accuracy, 2) the requirement of a large number of landmarks, 3) sensitivity to search range, 4) sensitivity to initialization, and 5) inability to fully exploit the specific information present in the given image to be segmented. OASM effectively combines the rich statistical shape information embodied in ASM with the boundary orientedness property and the globally optimal delineation capability of the live wire methodology of boundary segmentation. The latter characteristics allow live wire to effectively separate an object boundary from other nonobject boundaries with similar properties especially when they come very close in the image domain. The approach leads to a two-level dynamic programming method, wherein the first level corresponds to boundary recognition and the second level corresponds to boundary delineation, and to an effective automatic initialization method. The method outputs a globally optimal boundary that agrees with the shape model if the recognition step is successful in bringing the model close to the boundary in the image. Extensive evaluation experiments have been conducted by utilizing 40 image (magnetic resonance and computed tomography) data sets in each of five different application areas for segmenting breast, liver, bones of the foot, and cervical vertebrae of the spine. Comparisons are made between OASM and ASM based on precision, accuracy, and efficiency of segmentation. Accuracy is assessed using both region-based false positive and false negative measures and boundary-based distance measures. The results indicate the following: 1) The accuracy of segmentation via OASM is considerably better than that of ASM;2) The number of landmarks can

关键词： Active shape models (ASMs) dynamic programming image-based segmentation live wire model-based segmentation

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A comparison of stereology, structural rigidity and a novel 3D failure surface analysis method in the assessment of torsional strength and stiffness in a mouse tibia fracture model

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JOURNAL OF BIOMECHANICS 2012年第13期45卷 2236-2240页

作者： Wright, David A. Nam, Diane Whyne, Cari M. Sunnybrook Res Inst Orthopaed Biomech Lab UB 552075 Bayview Ave Toronto ON M4N 3MN Canada

In attempting to develop non-invasive image based measures for the determination of the biomechanical integrity of healing fractures, traditional mu CT based measurements have been limited. This study presents the development and evaluation of a tool for assessment of fracture callus mechanical properties through determination of the geometric characteristics of the fracture callus, specifically along the surface of failure identified during destructive mechanical testing. Fractures were created in tibias of ten male mice and subjected to mu CT imaging and biomechanical torsion testing. Failure surface analysis, along with previously described image based measures was calculated using the mu CT image data, and correlated with mechanical strength and stiffness. Three-dimensional measures along the surface of failure, specifically the surface area and torsional rigidity of bone, were shown to be significantly correlating with mechanical strength and stiffness. It was also shown that surface area of bone along the failure surface exhibits stronger correlations with both strength and stiffness than measures of average and minimum torsional rigidity of the entire callus. Failure surfaces observed in this study were generally oriented at 45 degrees to the long axis of the bone, and were not contained exclusively within the callus. This work represents a proof of concept study, and shows the potential utility of failure surface analysis in the assessment of fracture callus stability. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Long bone fracture healing Fracture healing image-based segmentation mu CT Mechanical testing Strength quantification

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Oriented Active Shape Models

Oriented Active Shape Models

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Medical Imaging 2006 Conference

作者： Liu, Jiamin Udupa, Jayaram K. Univ Penn Dept Bioengn Philadelphia PA 19104 USA Univ Penn Dept Radiol Med Image Proc Grp Philadelphia PA 19104 USA

ISBN: (纸本)0819464236

Active Shape Models (ASM) are widely employed for recognizing anatomic structures and for delineating them in medical images. In this paper, we present a novel strategy called Oriented Active Shape Models (OASM) in an attempt to overcome the following three major limitations of ASM: (1) poor delineation accuracy, (2) the requirement of a large number of landmarks, (3) the problem of sensitivity to search range to recognize the object boundary. OASM effectively combines the rich statistical shape information embodied in ASM with the boundary orientedness property and the globally optimal delineation capability of the live wire methodology of boundary segmentation. The latter allow live wire to effectively separate an object boundary from other non object boundaries with similar properties that come very close in the image domain. Our approach leads us to a 2-level dynamic programming method, wherein the first level corresponds to boundary recognition and the second level corresponds to boundary delineation. Our experiments in segmenting breast, liver, bones of the foot, and cervical vertebrae of the spine in MR and CT images indicate the following: (1) The accuracy of segmentation via OASM is considerably better than that of ASM. (2) The number of landmarks can be reduced by a factor of 3 in OASM over that in ASM. (3) OASM becomes largely independent of search range. All three benefits of OASM ensue mainly from the severe constraints brought in by the boundary-orientedness property of live wire and the globally optimal solution of dynamic programming.

关键词： model-based segmentation image-based segmentation active shape models live wire dynamic programming

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image-based Multiresolution Topology Optimization Using Deep Disjunctive Normal Shape Model

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COMPUTER-AIDED DESIGN 2021年 130卷 102947-102947页

作者： Keshavarzzadeh, Vahid Alirezaei, Mitra Tasdizen, Tolga Kirby, Robert M. Univ Utah Sci Comp & Imaging Inst Salt Lake City UT 84112 USA

We present a machine learning framework for predicting the optimized structural topology designs using multiresolution data. Our approach primarily uses optimized designs from inexpensive coarse mesh finite element simulations for model training and generates high resolution images associated with simulation parameters that are not previously used. Our cost-efficient approach enables the designers to effectively search through possible candidate designs in situations where the design requirements rapidly change. The underlying neural network framework is based on a deep disjunctive normal shape model (DDNSM) which learns the mapping between the simulation parameters and segments of multi resolution images. Using this image-based analysis we provide a practical algorithm which enhances the predictability of the learning machine by determining a limited number of important parametric samples (i.e. samples of the simulation parameters) on which the high resolution training data is generated. We demonstrate our approach on benchmark compliance minimization problems including the 3D topology optimization where we show that the high-fidelity designs from the learning machine are close to optimal designs and can be used as effective initial guesses for the large-scale optimization problem. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Topology optimization Multiresolution analysis image-based segmentation Deep neural networks

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