Cascaded MultiTask 3-D Fully Convolutional Networks for Pancreas Segmentation

Xue, Jie; He, Kelei; Nie, Dong; Adeli, Ehsan; Shi, Zhang; Lee, Seong–Whan; Zheng, Yuanjie; Liu, Xiyu; Li, Dengwang; Shen, Dinggang

doi:10.1109/tcyb.2019.2955178

“…For example, Seo et al proposed a modified U-Net [20] to segment liver and liver tumors. In [21], [22], CNNs were employed to segment the pancreas. In [23], a level set regression network was developed to obtain more accurate segmentation in pancreas boundaries.…”

Section: Abdominal Organ Segmentation Methodsmentioning

confidence: 99%

AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?

Ma

¹

,

Zhang

²

,

Gu

³

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs (i.e., liver, kidney, and spleen) seems to be a solved problem as the state-of-the-art (SOTA) methods have achieved comparable results with inter-observer variability on existing benchmark datasets. However, most of the existing abdominal organ segmentation benchmark datasets only contain single-center, single-phase, single-vendor, or single-disease cases, thus, it is unclear whether the excellent performance can generalize on more diverse datasets. In this paper, we present a large and diverse abdominal CT organ segmentation dataset, termed as AbdomenCT-1K, with more than 1000 (1K) CT scans from 11 countries, including multi-center, multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation, as well as reveal the unsolved segmentation problems of the SOTA method, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the introduction of the AbdomenCT-1K dataset will promote the future in-depth research towards clinical applicable abdominal organ segmentation methods. Moreover, the datasets, codes and trained models of baseline methods will be publicly available.

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“…For example, Seo et al proposed a modified U-Net [20] to segment liver and liver tumors. In [21], [22],…”

Section: Abdominal Organ Segmentation Methodsmentioning

confidence: 99%

AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?

Ma

¹

,

Zhang

²

,

Gu

³

et al. 2020

Preprint

View full text Add to dashboard Cite

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs (i.e., liver, kidney, and spleen) seems to be a solved problem as the state-of-the-art (SOTA) methods have achieved comparable results with inter-observer variability on existing benchmark datasets. However, most of the existing abdominal organ segmentation benchmark datasets only contain single-center, single-phase, single-vendor, or single-disease cases, thus, it is unclear whether the excellent performance can generalize on more diverse datasets. In this paper, we present a large and diverse abdominal CT organ segmentation dataset, termed as AbdomenCT-1K, with more than 1000 (1K) CT scans from 11 countries, including multi-center, multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation, as well as reveal the unsolved segmentation problems of the SOTA method, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the introduction of the AbdomenCT-1K dataset will promote the future in-depth research towards clinical applicable abdominal organ segmentation methods. Moreover, the datasets, codes and trained models of baseline methods will be publicly available.

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“…( 22 ) proposed spatial pyramid pooling (SPP) to solve the fixed input size caused by the fully connected layer and proposed the parallel extraction of multi-level features of SPP layer, which makes different size inputs have output with fixed dimension. PSPNet ( 23 ) applied multi-level feature extraction to the field of semantic segmentation. In its design of pyramid pooling module, four different sizes of pooling are fused and then stitched by a bilinear interpolation and a 1 × 1 convolution.…”

Section: Related Workmentioning

confidence: 99%

AX-Unet: A Deep Learning Framework for Image Segmentation to Assist Pancreatic Tumor Diagnosis

Yang

¹

,

Zhang

²

,

Chen

³

et al. 2022

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Image segmentation plays an essential role in medical imaging analysis such as tumor boundary extraction. Recently, deep learning techniques have dramatically improved performance for image segmentation. However, an important factor preventing deep neural networks from going further is the information loss during the information propagation process. In this article, we present AX-Unet, a deep learning framework incorporating a modified atrous spatial pyramid pooling module to learn the location information and to extract multi-level contextual information to reduce information loss during downsampling. We also introduce a special group convolution operation on the feature map at each level to achieve information decoupling between channels. In addition, we propose an explicit boundary-aware loss function to tackle the blurry boundary problem. We evaluate our model on two public Pancreas-CT datasets, NIH Pancreas-CT dataset, and the pancreas part in medical segmentation decathlon (MSD) medical dataset. The experimental results validate that our model can outperform the state-of-the-art methods in pancreas CT image segmentation. By comparing the extracted feature output of our model, we find that the pancreatic region of normal people and patients with pancreatic tumors shows significant differences. This could provide a promising and reliable way to assist physicians for the screening of pancreatic tumors.

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Cascaded MultiTask 3-D Fully Convolutional Networks for Pancreas Segmentation

Cited by 38 publications

References 44 publications

AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?

AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?

AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?

AX-Unet: A Deep Learning Framework for Image Segmentation to Assist Pancreatic Tumor Diagnosis

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