Segmentation, Separation and Pose Estimation of Prostate Brachytherapy Seeds in CT Images

Nguyen, Huu-Giao; Fouard, Céline; Troccaz, Jocelyne

doi:10.1109/tbme.2015.2409304

Cited by 12 publications

(10 citation statements)

References 38 publications

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“…This question was addressed by N’Guyen et al. for CT images, 5 and we could consider adapting their approach for the case of US images. Most methods of Table I focused on loose seeds except the recent deep learning‐based method 12 which was tested on strands.…”

Section: Discussionmentioning

confidence: 99%

“…Generally the CT images are used to perform postimplant dosimetry 1 month after seeds implantation. In this context, N’Guyen et al 5 . proposed an approach to determine seeds position and orientation in CT images using K‐means and principal component analysis (PCA) techniques; the method allows to separate seeds grouped in clusters, a situation that may occur with loose seeds.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning and registration for automatic seed localization in 3D US images for prostate brachytherapy

2021

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Purpose New radiation therapy protocols, in particular adaptive, focal or boost brachytherapy treatments, require determining precisely the position and orientation of the implanted radioactive seeds from real‐time ultrasound (US) images. This is necessary to compare them to the planned one and to adjust automatically the dosimetric plan accordingly for next seeds implantations. The image modality, the small size of the seeds, and the artifacts they produce make it a very challenging problem. The objective of the presented work is to setup and to evaluate a robust and automatic method for seed localization in three‐dimensional (3D) US images. Methods The presented method is based on a prelocalization of the needles through which the seeds are injected in the prostate. This prelocalization allows focusing the search on a region of interest (ROI) around the needle tip. Seeds localization starts by binarizing the ROI and removing false positives using, respectively, a Bayesian classifier and a support vector machine (SVM). This is followed by a registration stage using first an iterative closest point (ICP) for localizing the connected set of seeds (named strand) inserted through a needle, and secondly refining each seed position using sum of squared differences (SSD) as a similarity criterion. ICP registers a geometric model of the strand to the candidate voxels while SSD compares an appearance model of a single seed to a subset of the image. The method was evaluated both for 3D images of an Agar‐agar phantom and a dataset of clinical 3D images. It was tested on stranded and on loose seeds. Results Results on phantom and clinical images were compared with a manual localization giving mean errors of 1.09 ± 0.61 mm on phantom image and 1.44 ± 0.45 mm on clinical images. On clinical images, the mean errors of individual seeds orientation was 4.33±8.51∘. Conclusions The proposed algorithm for radioactive seed localization is robust, tested on different US images, accurate, giving small mean error values, and returns the five cylindrical seeds degrees of freedom.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning and registration for automatic seed localization in 3D US images for prostate brachytherapy

2021

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“…As illustrated in Fig. 1, medical image segmentation aims to identify tumors and delineate different sub-regions of organs from background, by assigning a predefined class label to each pixel in medical images, such as Magnetic Resonance Imaging (MRI) [2] and Computerized Tomography (CT) [3]. Traditionally, the lesion regions are mainly delineated by clinicians heavily relying on clinical experiences, which is time-consuming and prone to error.…”

Section: Introductionmentioning

confidence: 99%

TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images

Li¹,

Wang²,

Chen³

et al. 2022

Preprint

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Transformer, benefiting from global (longrange) information modeling using self-attention mechanism, has been successful in natural language processing and computer vision recently. Convolutional Neural Networks, capable of capturing local features, are unable to model explicit long-distance dependencies from global feature space. However, both local and global features are crucial for dense prediction tasks, especially for 3D medical image segmentation. In this paper, we exploit Transformer in 3D CNN for 3D medical image volumetric segmentation and propose a novel network named TransBTSV2 based on the encoder-decoder structure. Different from our original TransBTS [1], the proposed TransBTSV2 is not limited to brain tumor segmentation (BTS) but focuses on general medical image segmentation, providing a strong and efficient 3D baseline for volumetric segmentation of medical images. As a hybrid CNN-Transformer architecture, TransBTSV2 can achieve accurate segmentation of medical images without any pre-training. With the proposed insight to redesign the internal structure of Transformer and the introduced Deformable Bottleneck Module, a highly efficient architecture is achieved with superior performance. Extensive experimental results on four medical image datasets (BraTS 2019, BraTS 2020, LiTS 2017 and KiTS 2019) demonstrate that TransBTSV2 achieves comparable or better results as compared to the state-of-the-art methods for the segmentation of brain tumor, liver tumor as well as kidney tumor. Code is available at https://github.com/ Wenxuan-1119/TransBTS.

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“…A S one of the most challenging tasks in clinical diagnosis, the purpose of medical image segmentation is to identify the segmenting objects of interest from background medical images, such as X-ray [1], Computerized Tomography (CT) [2], Magnetic Resonance Imaging (MRI) [3], and Ultrasound [4]. As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation

Chen¹,

Liu²,

Zhang³

et al. 2021

Preprint

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Segmentation, Separation and Pose Estimation of Prostate Brachytherapy Seeds in CT Images

Abstract: Such a method is mandatory to be able to compute precisely the real dose delivered to the patient postoperatively instead of assuming the alignment of seeds along the theoretical insertion direction of the brachytherapy needles.

Cited by 12 publications

References 38 publications

Machine learning and registration for automatic seed localization in 3D US images for prostate brachytherapy

Machine learning and registration for automatic seed localization in 3D US images for prostate brachytherapy

TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images

TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation

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