An automatic segmentation approach for boundary delineation of corpus callosum based on cell competition

Keratocytes are vital for maintaining the overall health of human cornea as they preserve the corneal transparency and help in healing corneal injuries. Manual segmentation of keratocytes is challenging, time consuming and also needs an expert. Here, we propose a novel semi-automatic segmentation framework, called Conditional Random FieldWeakly Supervised Segmentation (CRF-WSS) to perform the keratocytes cell segmentation. The proposed framework exploits the concept of dictionary learning in a sparse model along with the Conditional Random Field (CRF) modeling to segment keratocytes cells in Ultra High Resolution Optical Coherence Tomography (UHR-OCT) images of human cornea. The results show higher accuracy for the proposed CRF-WSS framework compare to the other tested Supervised Segmentation (SS) andWeakly Supervised Segmentation (WSS) methods.

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Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET

10.21203/rs.3.rs-54809/v2 ◽

2020 ◽

Author(s):

Elisabeth Pfaehler ◽

Liesbet Mesotten ◽

Gem Kramer ◽

Michiel Thomeer ◽

Karolien Vanhove ◽

...

Keyword(s):

Artificial Intelligence ◽

Majority Vote ◽

Automatic Segmentation ◽

Intraclass Correlation ◽

Cancer Staging ◽

Tumor Segmentation ◽

Jaccard Coefficient ◽

Primary Tumors ◽

Segmentation Methods ◽

Segmentation Approach

Abstract Background: Positron Emission Tomography (PET) is routinely used for cancer staging and treatment follow up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV - including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis (TLG) derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end, a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge. Methods: In this study, we compare two semi-automatic artificial intelligence (AI) based segmentation methods with conventional semi-automatic segmentation approaches in terms of repeatability. One segmentation approach is based on a textural feature (TF) segmentation approach designed for accurate and repeatable segmentation of primary tumors and metastasis. Moreover, a Convolutional Neural Network (CNN) is trained. The algorithms are trained, validated and tested using a lung cancer PET dataset. The segmentation accuracy of both segmentation approaches is compared using the Jaccard Coefficient (JC). Additionally, the approaches are externally tested on a fully independent test-retest dataset. The repeatability of the methods is compared with those of two majority vote (MV2, MV3) approaches, 41%SUVMAX, and a SUV>4 segmentation (SUV4). Repeatability is assessed with test-retest coefficients (TRT%) and intraclass correlation coefficient (ICC). An ICC>0.9 was regarded as representing excellent repeatability.Results: The accuracy of the segmentations with the reference segmentation was good (JC median TF: 0.7, CNN: 0.73) Both segmentation approaches outperformed most other conventional segmentation methods in terms of test-retest coefficient (TRT% mean: TF: 13.0%, CNN: 13.9%, MV2: 14.1%, MV3: 28.1%, 41%SUVMAX: 28.1%, SUV4: 18.1% ) and ICC (TF: 0.98, MV2: 0.97, CNN: 0.99, MV3: 0.73, SUV4: 0.81, and 41%SUVMAX: 0.68).Conclusion: The semi-automatic AI based segmentation approaches used in this study provided better repeatability than conventional segmentation approaches. Moreover, both algorithms lead to accurate segmentations for both primary tumors as well as metastasis and are therefore good candidates for PET tumor segmentation.

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Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI

Applied Sciences ◽

10.3390/app10072601 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2601 ◽

Cited By ~ 1

Author(s):

Indriani P. Astono ◽

James S. Welsh ◽

Stephan Chalup ◽

Peter Greer

Keyword(s):

State Of The Art ◽

Automatic Segmentation ◽

Feature Maps ◽

The Public ◽

Prostate Segmentation ◽

Segmentation Approach ◽

Model Components ◽

The Individual ◽

Deep Learning Model ◽

Better Than

In this paper, we develop an optimised state-of-the-art 2D U-Net model by studying the effects of the individual deep learning model components in performing prostate segmentation. We found that for upsampling, the combination of interpolation and convolution is better than the use of transposed convolution. For combining feature maps in each convolution block, it is only beneficial if a skip connection with concatenation is used. With respect to pooling, average pooling is better than strided-convolution, max, RMS or L2 pooling. Introducing a batch normalisation layer before the activation layer gives further performance improvement. The optimisation is based on a private dataset as it has a fixed 2D resolution and voxel size for every image which mitigates the need of a resizing operation in the data preparation process. Non-enhancing data preprocessing was applied and five-fold cross-validation was used to evaluate the fully automatic segmentation approach. We show it outperforms the traditional methods that were previously applied on the private dataset, as well as outperforming other comparable state-of-the-art 2D models on the public dataset PROMISE12.

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SEGMA: An Automatic SEGMentation Approach for Human Brain MRI Using Sliding Window and Random Forests

Frontiers in Neuroinformatics ◽

10.3389/fninf.2017.00002 ◽

2017 ◽

Vol 11 ◽

Cited By ~ 7

Author(s):

Ahmed Serag ◽

Alastair G. Wilkinson ◽

Emma J. Telford ◽

Rozalia Pataky ◽

Sarah A. Sparrow ◽

...

Keyword(s):

Human Brain ◽

Random Forests ◽

Automatic Segmentation ◽

Brain Mri ◽

Sliding Window ◽

Segmentation Approach

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CLASSIFICATION AND INTEGRATION OF MASSIVE 3D POINTS CLOUDS IN A VIRTUAL REALITY (VR) ENVIRONMENT

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w17-165-2019 ◽

2019 ◽

Vol XLII-2/W17 ◽

pp. 165-171 ◽

Cited By ~ 3

Author(s):

A. Kharroubi ◽

R. Hajji ◽

R. Billen ◽

F. Poux

Keyword(s):

Virtual Reality ◽

Open Source ◽

Point Cloud ◽

Mixed Reality ◽

Automatic Segmentation ◽

Point Clouds ◽

3D Point Clouds ◽

3D Data ◽

Segmentation Approach ◽

And Performance

Abstract. With the increasing volume of 3D applications using immersive technologies such as virtual, augmented and mixed reality, it is very interesting to create better ways to integrate unstructured 3D data such as point clouds as a source of data. Indeed, this can lead to an efficient workflow from 3D capture to 3D immersive environment creation without the need to derive 3D model, and lengthy optimization pipelines. In this paper, the main focus is on the direct classification and integration of massive 3D point clouds in a virtual reality (VR) environment. The emphasis is put on leveraging open-source frameworks for an easy replication of the findings. First, we develop a semi-automatic segmentation approach to provide semantic descriptors (mainly classes) to groups of points. We then build an octree data structure leveraged through out-of-core algorithms to load in real time and continuously only the points that are in the VR user's field of view. Then, we provide an open-source solution using Unity with a user interface for VR point cloud interaction and visualisation. Finally, we provide a full semantic VR data integration enhanced through developed shaders for future spatio-semantic queries. We tested our approach on several datasets of which a point cloud composed of 2.3 billion points, representing the heritage site of the castle of Jehay (Belgium). The results underline the efficiency and performance of the solution for visualizing classifieds massive point clouds in virtual environments with more than 100 frame per second.

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Pulmonary Dynamics of Anatomical Structures of Interest in 4DCT Images

10.17488/rmib.30.1.9 ◽

2017 ◽

Author(s):

◽

S. Hernández Juárez

Keyword(s):

Lung Tumor ◽

Automatic Segmentation ◽

Spatial Location ◽

Maximum Displacement ◽

Voxel Size ◽

Anatomical Structures ◽

Size Information ◽

Segmentation Approach ◽

Near Future ◽

Clinical Expert

This paper presents an application of the Chan-Vese algorithm for a semi-automatic segmentation of anatomical structures of interest (lungs and lung tumor) in thorax 4DCT images, as well as its threedimensional reconstruction. Segmentations and reconstructions were performed in 10 CT images, which conform an inspiration-expiration cycle. The maximum displacement of the lung tumor was calculated using the reconstructions of the beginning of inspiration, beginning of expiration, and the voxel size information. The proposed method was able to succesfully segment the studied structures regardless of their size and shape. The threedimensional reconstruction allow us to visualize the dynamics of the structures of interest throughout the respiratory cycle. In the near future, we are expecting to be able to have more evidence of the good performance of the proposed segmentation approach, and to have feedback from a clinical expert, giving the fact that the knowledge of anatomical structures characteristics, such as their size and spatial location, may help in the planning of radiotherapy treatments (RT), optimizing the radiation dose to cancer cells and minimizing it in healthy organs. Therefore, the information found in this work maybe of interest for the planning of RT treatments.

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