Enhancing organ at risk segmentation with improved deep neural networks

Isler, Ilkin; Lisle, Curtis; Rineer, Justin; Kelly, Patrick; Turgut, Damla; Ricci, Jacob; Bağcı, Ulaş

doi:10.1117/12.2611498

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…3.1.3. The proposed OAR-TRANSEG versus state-of-the-art methodWe compared our proposed model (OAR-TRANSEG) with the state-of-the-art model (3D ResU-Net)(Isler et al 2022), which is a convolutional network with residual blocks evaluated on the OpenKBP dataset, to show the superiority of our model in the OARs segmentation task. Since the 3D ResU-Net model considered only the five OARs from the OpenKBP dataset, we conducted a comparison of these five OARs for fairness.…”

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confidence: 99%

A cascade transformer-based model for 3D dose distribution prediction in head and neck cancer radiotherapy

Gheshlaghi,

Nabavi,

Shirzadikia

et al. 2024

Phys. Med. Biol.

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Objective: Radiation therapy is one of the primary methods used to treat cancer in the clinic. Its goal is to deliver a precise dose to the planning target volume (PTV) while protecting the surrounding organs at risk (OARs). However, the traditional workflow used by dosimetrists to plan the treatment is time-consuming and subjective, requiring iterative adjustments based on their experience. Deep learning methods can be used to predict dose distribution maps to address these limitations. Approach: The study proposes a cascade model for organs at risk segmentation and dose distribution prediction. An encoder–decoder network has been developed for the segmentation task, in which the encoder consists of transformer blocks, and the decoder uses multi–scale convolutional blocks. Another cascade encoder–decoder network has been proposed for dose distribution prediction using a pyramid architecture. The proposed model has been evaluated using an in–house head and neck cancer dataset of 96 patients and OpenKBP, a public head and neck cancer dataset of 340 patients. Main results: The segmentation subnet achieved 0.79 and 2.71 for Dice and HD95 scores, respectively. This subnet outperformed the existing baselines. The dose distribution prediction subnet outperformed the winner of the OpenKBP2020 competition with 2.77 and 1.79 for dose and DVH scores, respectively. Besides, the end-to-end model, including both subnets simultaneously, outperformed the related studies.Significance: The predicted dose maps showed good coincidence with ground-truth, with a superiority after linking with the auxiliary segmentation task. The proposed model outperformed state-of-the-art methods, especially in regions with low prescribed doses. The codes are available at https://github.com/GhTara/Dose_Prediction.

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confidence: 99%

A cascade transformer-based model for 3D dose distribution prediction in head and neck cancer radiotherapy

Gheshlaghi,

Nabavi,

Shirzadikia

et al. 2024

Phys. Med. Biol.

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An Efficient Multi-Scale Fusion Network for 3D Organs at Risk (OARs) Segmentation

Srivastava,

Jha,

Keles

et al. 2023

2023 45th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation

Liu,

Qu,

Xie

et al. 2024

BioMed Eng OnLine

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Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation methods have far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. We searched Google Scholar for papers published from January 1, 2016 to December 31, 2023, using keywords “multi-organ segmentation” and “deep learning”, resulting in 327 papers. We followed the PRISMA guidelines for paper selection, and 195 studies were deemed to be within the scope of this review. We summarized the two main aspects involved in multi-organ segmentation: datasets and methods. Regarding datasets, we provided an overview of existing public datasets and conducted an in-depth analysis. Concerning methods, we categorized existing approaches into three major classes: fully supervised, weakly supervised and semi-supervised, based on whether they require complete label information. We summarized the achievements of these methods in terms of segmentation accuracy. In the discussion and conclusion section, we outlined and summarized the current trends in multi-organ segmentation.

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Enhancing organ at risk segmentation with improved deep neural networks

Cited by 3 publications

References 4 publications

A cascade transformer-based model for 3D dose distribution prediction in head and neck cancer radiotherapy

A cascade transformer-based model for 3D dose distribution prediction in head and neck cancer radiotherapy

An Efficient Multi-Scale Fusion Network for 3D Organs at Risk (OARs) Segmentation

Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation

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