Technical Note: A deep learning‐based autosegmentation of rectal tumors in <scp>MR</scp> images

Wang, Jiazhou; Lu, Jiayu; Gan, Qintao; Shen, Lijun; Sun, Yiqun; Ying, Hongmei; Zhang, Zhen; Hu, Wenyu

doi:10.1002/mp.12918

Cited by 90 publications

(53 citation statements)

References 18 publications

(19 reference statements)

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“…Segmentation methods based on deep learning can be handled by supervised learning with adequate training data . To build a reliable segmentation model, a prerequisite is the availability of a large amount of labeled training data.…”

Section: Deep‐learning Methodsmentioning

confidence: 99%

Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state‐of‐art applications

et al. 2020

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In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. In this review article, we highlight the imperative role of machine learning algorithms in enabling efficient and accurate segmentation in the field of medical imaging. We specifically focus on several key studies pertaining to the application of machine learning methods to biomedical image segmentation. We review classical machine learning algorithms such as Markov random fields, k‐means clustering, random forest, etc. Although such classical learning models are often less accurate compared to the deep‐learning techniques, they are often more sample efficient and have a less complex structure. We also review different deep‐learning architectures, such as the artificial neural networks (ANNs), the convolutional neural networks (CNNs), and the recurrent neural networks (RNNs), and present the segmentation results attained by those learning models that were published in the past 3 yr. We highlight the successes and limitations of each machine learning paradigm. In addition, we discuss several challenges related to the training of different machine learning models, and we present some heuristics to address those challenges.

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Section: Deep‐learning Methodsmentioning

confidence: 99%

Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state‐of‐art applications

et al. 2020

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“…Initially, as naive practices, part based FCNs learn from local parts of 2D slices [7,15,16], 2.5D slices [17,18] or small 3D patches [10,19] and perform (often overlapped) part-sliding for whole volume inference, which is slow and prone to false positives and target incompleteness related failures. More importantly, part based methods suffer from limited effective receptive fields.…”

Section: Introductionmentioning

confidence: 99%

3-D RoI-Aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation

Huang

Dou

Wang

et al. 2021

IEEE Trans. Cybern.

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Segmentation of colorectal cancerous regions from 3DMagnetic Resonance (MR) images is a crucial procedure for radiotherapy which conventionally requires accurate delineation of tumour boundaries at an expense of labor, time and reproducibility. While deep learning based methods serve good baselines in 3D image segmentation tasks, small applicable patch size limits effective receptive field and degrades segmentation performance. In addition, Regions of interest (RoIs) localization from large whole volume 3D images serves as a preceding operation that brings about multiple benefits in terms of speed, target completeness, reduction of false positives. Distinct from sliding window or non-joint localizationsegmentation based models, we propose a novel multitask framework referred to as 3D RoI-aware U-Net (3D RU-Net), for RoI localization and in-region segmentation where the two tasks share one backbone encoder network. With the region proposals from the encoder, we crop multi-level RoI in-region features from the encoder to form a GPU memory-efficient decoder for detailpreserving segmentation and therefore enlarged applicable volume size and effective receptive field. To effectively train the model, we designed a Dice formulated loss function for the global-to-local multi-task learning procedure. Based on the efficiency gains demonstrated by the proposed method, we went on to ensemble models with different receptive fields to achieve even higher performance costing minor extra computational expensiveness. Extensive experiments were subsequently conducted on 64 cancerous cases with a four-fold cross-validation, and the results showed significant superiority in terms of accuracy and efficiency over conventional state-of-the art frameworks. In conclusion, the proposed method has a huge potential for extension to other 3D object segmentation tasks from medical images due to its inherent generalizability. The code for the proposed method is publicly available.3D CNN, region of interest, multi-task learning, tumor segmentation, colorectal cancer.

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“…Given an input image, both models can provide a segmentation mask with the same size of the input image. The image‐to‐image manner enhances the segmentation performance by utilizing features learned from different levels of the deep learning models . More important, given the encouraging whole‐breast segmentation performance of deep learning on the DCE MRI images, and considering that labeled DWI images are limited in reality, we leveraged the transfer learning scheme to take advantage of the good performance of deep learning models on DCE MRI images .…”

Section: Methodsmentioning

confidence: 99%

Automated deep learning method for whole‐breast segmentation in diffusion‐weighted breast MRI

Zhang

Mohamed

Chai

et al. 2019

Magnetic Resonance Imaging

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Background Diffusion‐weighted imaging (DWI) in MRI plays an increasingly important role in diagnostic applications and developing imaging biomarkers. Automated whole‐breast segmentation is an important yet challenging step for quantitative breast imaging analysis. While methods have been developed on dynamic contrast‐enhanced (DCE) MRI, automatic whole‐breast segmentation in breast DWI MRI is still underdeveloped. Purpose To develop a deep/transfer learning‐based segmentation approach for DWI MRI scans and conduct an extensive study assessment on four imaging datasets from both internal and external sources. Study Type Retrospective. Subjects In all, 98 patients (144 MRI scans; 11,035 slices) of four different breast MRI datasets from two different institutions. Field Strength/Sequences 1.5T scanners with DCE sequence (Dataset 1 and Dataset 2) and DWI sequence. A 3.0T scanner with one external DWI sequence. Assessment Deep learning models (UNet and SegNet) and transfer learning were used as segmentation approaches. The main DCE Dataset (4,251 2D slices from 39 patients) was used for pre‐training and internal validation, and an unseen DCE Dataset (431 2D slices from 20 patients) was used as an independent test dataset for evaluating the pre‐trained DCE models. The main DWI Dataset (6,343 2D slices from 75 MRI scans of 29 patients) was used for transfer learning and internal validation, and an unseen DWI Dataset (10 2D slices from 10 patients) was used for independent evaluation to the fine‐tuned models for DWI segmentation. Manual segmentations by three radiologists (>10‐year experience) were used to establish the ground truth for assessment. The segmentation performance was measured using the Dice Coefficient (DC) for the agreement between manual expert radiologist's segmentation and algorithm‐generated segmentation. Statistical Tests The mean value and standard deviation of the DCs were calculated to compare segmentation results from different deep learning models. Results For the segmentation on the DCE MRI, the average DC of the UNet was 0.92 (cross‐validation on the main DCE dataset) and 0.87 (external evaluation on the unseen DCE dataset), both higher than the performance of the SegNet. When segmenting the DWI images by the fine‐tuned models, the average DC of the UNet was 0.85 (cross‐validation on the main DWI dataset) and 0.72 (external evaluation on the unseen DWI dataset), both outperforming the SegNet on the same datasets. Data Conclusion The internal and independent tests show that the deep/transfer learning models can achieve promising segmentation effects validated on DWI data from different institutions and scanner types. Our proposed approach may provide an automated toolkit to help computer‐aided quantitative analyses of breast DWI images. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:635–643.

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Technical Note: A deep learning‐based autosegmentation of rectal tumors in MR images

Abstract: This study showed that a simple deep learning neural network can perform segmentation for rectum cancer based on MRI T2 images with results comparable to a human.

Cited by 90 publications

References 18 publications

Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state‐of‐art applications

Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state‐of‐art applications

3-D RoI-Aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation

Automated deep learning method for whole‐breast segmentation in diffusion‐weighted breast MRI

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