Supervised and Semi-supervised Methods for Abdominal Organ Segmentation: A Review

Senkyire, Isaac Baffour; Liu, Zhe

doi:10.1007/s11633-021-1313-0

Cited by 13 publications

(4 citation statements)

References 173 publications

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“…Some approaches simplify multi-organ segmentation to multiple independent single-organ segmentation tasks (Larsson et al 2018, which has been experimentally verified to be effective for localizing and segmenting the multi-organ (Senkyire and Liu 2021), especially some challenging organs, such as the pancreas (Ma et al 2021a). Yet, such approaches ignore the inter-organ spatial correlation, therefore, how to make full use of such contextual information, i.e.…”

Section: Overview Of Multi-organ Segmentation and Problem Analysismentioning

confidence: 99%

Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation

Li,

Wang,

Meng

et al. 2024

Phys. Med. Biol.

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Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning. Thus, it is of great significance to explore automatic segmentation approaches, among which deep learning-based approaches have evolved rapidly and witnessed remarkable progress in multi-organ segmentation. However, obtaining an appropriately sized and fine-grained annotated dataset of multiple organs is extremely hard and expensive. Such scarce annotation limits the development of high-performance multi-organ segmentation models but promotes many annotation-efficient learning paradigms. Among these, studies on transfer learning leveraging external datasets, semi-supervised learning using unannotated datasets and partially-supervised learning integrating partially-labeled datasets have led the dominant way to break such dilemma in multi-organ segmentation. We first review the traditional fully supervised method, then present a comprehensive and systematic elaboration of the 3 abovementioned learning paradigms in the context of multi-organ segmentation from both technical and methodological perspectives, and finally summarize their challenges and future trends.

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Section: Overview Of Multi-organ Segmentation and Problem Analysismentioning

confidence: 99%

Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation

Li,

Wang,

Meng

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

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“…Data-insufficient learning. It is promising to explore efficient learning strategies [83,84] under limited conditions in specific clinical applications, such as weakly-supervised/un-supervised/self-supervised learning and knowledge distillation.…”

Section: Potential Directionsmentioning

confidence: 99%

Video Polyp Segmentation: A Deep Learning Perspective

Xiao

Chou

et al. 2022

Mach. Intell. Res.

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We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era. Over the years, developments in VPS are not moving forward with ease due to the lack of a large-scale dataset with fine-grained segmentation annotations. To address this issue, we first introduce a high-quality frame-by-frame annotated VPS dataset, named SUN-SEG, which contains 158 690 colonoscopy video frames from the well-known SUN-database. We provide additional annotation covering diverse types, i.e., attribute, object mask, boundary, scribble, and polygon. Second, we design a simple but efficient baseline, named PNS+, which consists of a global encoder, a local encoder, and normalized self-attention (NS) blocks. The global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations, which are then progressively refined by two NS blocks. Extensive experiments show that PNS+ achieves the best performance and real-time inference speed (170 fps), making it a promising solution for the VPS task. Third, we extensively evaluate 13 representative polyp/object segmentation models on our SUN-SEG dataset and provide attribute-based comparisons. Finally, we discuss several open issues and suggest possible research directions for the VPS community. Our project and dataset are publicly available at https://github.com/GewelsJI/VPS.

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“…More and more semi-supervised segmentation methods have been proposed in recent years to confront the challenge of difficult access to annotated data [45]. Self-training is one of the most commonly used semi-supervised methods [46]. It first trains using a small amount of labeled data, then makes predictions on unlabeled data, and finally mixes the excellent predictions with labeled data for training [47,48].…”

Section: Semi-supervisedmentioning

confidence: 99%

CAFS: An Attention-Based Co-Segmentation Semi-Supervised Method for Nasopharyngeal Carcinoma Segmentation

Chen

Han

Lin

et al. 2022

Sensors

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Accurate segmentation of nasopharyngeal carcinoma is essential to its treatment effect. However, there are several challenges in existing deep learning-based segmentation methods. First, the acquisition of labeled data are challenging. Second, the nasopharyngeal carcinoma is similar to the surrounding tissues. Third, the shape of nasopharyngeal carcinoma is complex. These challenges make the segmentation of nasopharyngeal carcinoma difficult. This paper proposes a novel semi-supervised method named CAFS for automatic segmentation of nasopharyngeal carcinoma. CAFS addresses the above challenges through three mechanisms: the teacher–student cooperative segmentation mechanism, the attention mechanism, and the feedback mechanism. CAFS can use only a small amount of labeled nasopharyngeal carcinoma data to segment the cancer region accurately. The average DSC value of CAFS is 0.8723 on the nasopharyngeal carcinoma segmentation task. Moreover, CAFS has outperformed the state-of-the-art nasopharyngeal carcinoma segmentation methods in the comparison experiment. Among the compared state-of-the-art methods, CAFS achieved the highest values of DSC, Jaccard, and precision. In particular, the DSC value of CAFS is 7.42% higher than the highest DSC value in the state-of-the-art methods.

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Supervised and Semi-supervised Methods for Abdominal Organ Segmentation: A Review

Cited by 13 publications

References 173 publications

Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation

Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation

Video Polyp Segmentation: A Deep Learning Perspective

CAFS: An Attention-Based Co-Segmentation Semi-Supervised Method for Nasopharyngeal Carcinoma Segmentation

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