Recurrent Saliency Transformation Network for Tiny Target Segmentation in Abdominal CT Scans

Xie, Lingxi; Yu, Qihang; Zhou, Yuyin; Wang, Yan; Fishman, Elliot K.; Yuille, Alan

doi:10.1109/tmi.2019.2930679

Cited by 60 publications

(16 citation statements)

References 36 publications

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“…In their attempt to create an algorithm for automated PCL segmentation, Zhou et al trained their initial model on their own dataset of 131 cystic pancreas segmentations, achieving a DSC of 63.44 ± 27.71% for cyst segmentation at testing in 2017 [ 26 ], and 68.98 ± 26.68% with their most recent algorithm [ 28 ]. As the aim of the study at hand was PCL detection, the results cannot be directly compared.…”

Section: Discussionmentioning

confidence: 99%

“…The current state of the art for the automatic segmentation of the pancreas uses organ-attention networks with reverse connections to achieve a mean Dice-Sørensen coefficient (DSC) of 87.8 ± 3.1% [ 27 ]. The number of algorithms for the challenging detection of pancreatic cysts are very limited and none are clinically implemented [ 26 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

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Automated Detection of Pancreatic Cystic Lesions on CT Using Deep Learning

et al. 2021

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Pancreatic cystic lesions (PCL) are a frequent and underreported incidental finding on CT scans and can transform into neoplasms with devastating consequences. We developed and evaluated an algorithm based on a two-step nnU-Net architecture for automated detection of PCL on CTs. A total of 543 cysts on 221 abdominal CTs were manually segmented in 3D by a radiology resident in consensus with a board-certified radiologist specialized in abdominal radiology. This information was used to train a two-step nnU-Net for detection with the performance assessed depending on lesions’ volume and location in comparison to three human readers of varying experience. Mean sensitivity was 78.8 ± 0.1%. The sensitivity was highest for large lesions with 87.8% for cysts ≥220 mm3 and for lesions in the distal pancreas with up to 96.2%. The number of false-positive detections for cysts ≥220 mm3 was 0.1 per case. The algorithm’s performance was comparable to human readers. To conclude, automated detection of PCL on CTs is feasible. The proposed model could serve radiologists as a second reading tool. All imaging data and code used in this study are freely available online.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Detection of Pancreatic Cystic Lesions on CT Using Deep Learning

et al. 2021

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“…Therefore, there is a growing need to develop AI-based algorithms for accurate pancreatic tumor detection. Although deep learning has been investigated for the diagnosis of pancreatic cystic neoplasms[ 47 ], neuroendocrine tumors[ 48 ] and segmentation of the pancreas[ 49 - 52 ], the usefulness of AI in the detection of PDAC has not yet been widely explored. AI can analyze thousands of images on a pixel-by-pixel level and is not susceptible to mistakes due to human error.…”

Section: Detection Of Early Pdac By Radiomicsmentioning

confidence: 99%

Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma

Hayashi¹,

Uemura²,

Matsumura³

et al. 2021

WJG

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Pancreatic ductal adenocarcinoma (PDAC) remains the most lethal type of cancer. The 5-year survival rate for patients with early-stage diagnosis can be as high as 20%, suggesting that early diagnosis plays a pivotal role in the prognostic improvement of PDAC cases. In the medical field, the broad availability of biomedical data has led to the advent of the “big data” era. To overcome this deadly disease, how to fully exploit big data is a new challenge in the era of precision medicine. Artificial intelligence (AI) is the ability of a machine to learn and display intelligence to solve problems. AI can help to transform big data into clinically actionable insights more efficiently, reduce inevitable errors to improve diagnostic accuracy, and make real-time predictions. AI-based omics analyses will become the next alterative approach to overcome this poor-prognostic disease by discovering biomarkers for early detection, providing molecular/genomic subtyping, offering treatment guidance, and predicting recurrence and survival. Advances in AI may therefore improve PDAC survival outcomes in the near future. The present review mainly focuses on recent advances of AI in PDAC for clinicians. We believe that breakthroughs will soon emerge to fight this deadly disease using AI-navigated precision medicine.

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“…Global information and local details can be independently processed. For instance, regions of pancreas are localised with a deep Q network and then segmented with a deformable U‐net [41]; shape details of target regions are recovered with saliency transformation modules and refined recurrently with a finer‐scaled network [66]; cell‐level architecture search spaces and network‐level ones are used to optimise the semantic segmentation architecture [27].…”

Section: Related Workmentioning

confidence: 99%