A geometric method for the detection and correction of segmentation leaks of anatomical structures in volumetric medical images

Kronman, Achia; Joskowicz, Leo

doi:10.1007/s11548-015-1285-z

Cited by 17 publications

(6 citation statements)

References 16 publications

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“…The ambiguous boundary with a limited contrast between targeting organs and the neighboring tissues is a known inherent imaging challenge. This is usually caused by attenuation coefficient in CT and relaxation time in MRI [23, 46]. Multi-modality-based approaches can address this problem [57, 76, 87, 89].…”

Section: Challenges and State-of-the-art Solutionsmentioning

confidence: 99%

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

et al. 2019

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Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.

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Section: Challenges and State-of-the-art Solutionsmentioning

confidence: 99%

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

et al. 2019

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“…Using deeper architectures can be an effective solution to this issue, as reported in [ 115 ]. The unclear border with an imperfect contrast among targeting organs and the nearby tissues in tumor images is an inherent challenge typically produced via attenuation coefficient [ 150 , 151 ]. The use of multi-modality-based methods can solve this issue [ 152 , 153 ]; The computational complexity of the network is another challenge in DL-based techniques, owing to variability in image dimensions, network construction, or the heavily over-parameterized networks.…”

Section: Discussionmentioning

confidence: 99%

State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods

Ali

Pei

et al. 2021

Cancers

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Thus far, the most common cause of death in the world is cancer. It consists of abnormally expanding areas that are threatening to human survival. Hence, the timely detection of cancer is important to expanding the survival rate of patients. In this survey, we analyze the state-of-the-art approaches for multi-organ cancer detection, segmentation, and classification. This article promptly reviews the present-day works in the breast, brain, lung, and skin cancer domain. Afterwards, we analytically compared the existing approaches to provide insight into the ongoing trends and future challenges. This review also provides an objective description of widely employed imaging techniques, imaging modality, gold standard database, and related literature on each cancer in 2016–2021. The main goal is to systematically examine the cancer diagnosis systems for multi-organs of the human body as mentioned. Our critical survey analysis reveals that greater than 70% of deep learning researchers attain promising results with CNN-based approaches for the early diagnosis of multi-organ cancer. This survey includes the extensive discussion part along with current research challenges, possible solutions, and prospects. This research will endow novice researchers with valuable information to deepen their knowledge and also provide the room to develop new robust computer-aid diagnosis systems, which assist health professionals in bridging the gap between rapid diagnosis and treatment planning for cancer patients.

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“…In order to improve the performance, we have proposed a novel algorithm based on 3D multiscale densely connected neural network (3D MSDenseNet). Many studies were carried out in the literature to develop techniques for medical image segmentation; they are mostly based on geometrical methods to address the hurdles and challenges for the segmentation of medical imaging, including statistical shape models, graph cuts, level set, and so on [34]. Recently, level set-based segmentation algorithms were commonly explored approaches for medical image segmentations.…”

Section: Discussionmentioning

confidence: 99%

Automated Segmentation of Colorectal Tumor in 3D MRI Using 3D Multiscale Densely Connected Convolutional Neural Network

Soomro

Coppotelli

Conforto

et al. 2019

Journal of Healthcare Engineering

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The main goal of this work is to automatically segment colorectal tumors in 3D T2-weighted (T2w) MRI with reasonable accuracy. For such a purpose, a novel deep learning-based algorithm suited for volumetric colorectal tumor segmentation is proposed. The proposed CNN architecture, based on densely connected neural network, contains multiscale dense interconnectivity between layers of fine and coarse scales, thus leveraging multiscale contextual information in the network to get better flow of information throughout the network. Additionally, the 3D level-set algorithm was incorporated as a postprocessing task to refine contours of the network predicted segmentation. The method was assessed on T2-weighted 3D MRI of 43 patients diagnosed with locally advanced colorectal tumor (cT3/T4). Cross validation was performed in 100 rounds by partitioning the dataset into 30 volumes for training and 13 for testing. Three performance metrics were computed to assess the similarity between predicted segmentation and the ground truth (i.e., manual segmentation by an expert radiologist/oncologist), including Dice similarity coefficient (DSC), recall rate (RR), and average surface distance (ASD). The above performance metrics were computed in terms of mean and standard deviation (mean ± standard deviation). The DSC, RR, and ASD were 0.8406 ± 0.0191, 0.8513 ± 0.0201, and 2.6407 ± 2.7975 before postprocessing, and these performance metrics became 0.8585 ± 0.0184, 0.8719 ± 0.0195, and 2.5401 ± 2.402 after postprocessing, respectively. We compared our proposed method to other existing volumetric medical image segmentation baseline methods (particularly 3D U-net and DenseVoxNet) in our segmentation tasks. The experimental results reveal that the proposed method has achieved better performance in colorectal tumor segmentation in volumetric MRI than the other baseline techniques.

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A geometric method for the detection and correction of segmentation leaks of anatomical structures in volumetric medical images

Abstract: The advantages of our method are that it is independent of the initial segmentation algorithm that covers a variety of anatomical structures and pathologies, that it does not require a shape prior, and that it requires minimal user interaction.

Cited by 17 publications

References 16 publications

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods

Automated Segmentation of Colorectal Tumor in 3D MRI Using 3D Multiscale Densely Connected Convolutional Neural Network

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