Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM

Zheng, Rencheng; Wang, Qidong; Lv, Shihui; Li, Cuiping; Wang, Chenyan; Chen, Weibo; Wang, He

doi:10.1109/tmi.2022.3175461

Cited by 50 publications

(32 citation statements)

References 53 publications

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“…Finally, of the 21 papers that adopted an independent test set, only 3 of them (amounting to 6% of the 51 surveyed papers) reported confidence intervals or standard-error [13,14,15]. Note that 11 other papers used statistical testing to compare different approaches even though they did not provide confidence intervals [16,17,18,19,20,21,22,23,24,25,26].…”

Section: Discussionmentioning

confidence: 99%

How precise are performance estimates for typical medical image segmentation tasks?

Jurdi¹,

Colliot²

2022

Preprint

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An important issue in medical image processing is to be able to estimate not only the performances of algorithms but also the precision of the estimation of these performances. Reporting precision typically amounts to reporting standard-error of the mean (SEM) or equivalently confidence intervals. However, this is rarely done in medical image segmentation studies. In this paper, we aim to estimate what is the typical confidence that can be expected in such studies. To that end, we first perform experiments for Dice metric estimation using a standard deep learning model (U-net) and a classical task from the Medical Segmentation Decathlon. We extensively study precision estimation using both Gaussian assumption and bootstrapping (which does not require any assumption on the distribution). We then perform simulations for other test set sizes and performance spreads. Overall, our work shows that small test sets lead to wide confidence intervals (e.g. ∼8 points of Dice for 20 samples with σ 10).

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Section: Discussionmentioning

confidence: 99%

How precise are performance estimates for typical medical image segmentation tasks?

Jurdi¹,

Colliot²

2022

Preprint

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“…In Zheng et al (2022), a 4D DL network built upon 3D convolution and convolutional long short-term memory (LSTM), namely Conv-LSTM, for HCC lesion segmentation. This DL module uses 4D data corresponding to dynamic CE-MRI images to assist liver tumor segmentation.…”

Section: D Inputmentioning

confidence: 99%

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Al‐Kababji

Bensaali

Dakua

et al. 2023

Engineering Applications of Artificial Intelligence

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“…Zheng, R., et al [ 19 ] apply a 4D deep learning model that takes advantage of 3D memory to the issue of lesion segmentation: The projected deep learning approach uses the four-dimensionality of DCE MRI to aid in liver tumour segmentation. A shallow U-net-based 3D CNN module was utilised to extract the DCE phases’ 3D spatial area features, and a 4-layer C-LSTM network module was employed to make use of the phases’ temporal domain information.…”

Section: Related Workmentioning

confidence: 99%

Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model

et al. 2023

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Systems for medical analytics and decision making that make use of multimodal intelligence are of critical importance in the field of healthcare. Liver cancer is one of the most frequent types of cancer and early identification of it is crucial for effective therapy. Liver tumours share the same brightness and contrast characteristics as their surrounding tissues. Likewise, irregular tumour shapes are a serious concern that varies with cancer stage and tumour kind. There are two main phases of tumour segmentation in the liver: identifying the liver, and then segmenting the tumour itself. Conventional interactive segmentation approaches, however, necessitate a high number of intensity levels, whereas recently projected CNN-based interactive segmentation approaches are constrained by low presentation on liver tumour images. This research provides a unique deep Learning based Segmentation with Coot Extreme Learning Model approach that shows high efficiency in results and also detects tumours from the publicly available data of liver images. Specifically, the study processes the initial segmentation with a small number of additional users clicks to generate an improved segmentation by incorporating inner boundary points through the proposed geodesic distance encoding method. Finally, classification is carried out using an Extreme Learning Model, with the classifier’s parameters having been ideally chosen by means of the Coot Optimization algorithm (COA). On the 3D-IRCADb1 dataset, the research evaluates the segmentation quality metrics DICE and accuracy, finding improvements over approaches in together liver-coloured and tumour separation.

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Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM

Cited by 50 publications

References 53 publications

How precise are performance estimates for typical medical image segmentation tasks?

How precise are performance estimates for typical medical image segmentation tasks?

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model

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