Automated liver lesion detection in CT images based on multi-level geometric features

2013 ◽  
Vol 9 (4) ◽  
pp. 577-593 ◽  
Author(s):  
László Ruskó ◽  
Ádám Perényi
Keyword(s):  
Liver Lesion ◽  
Ct Images ◽  
Lesion Detection ◽  
Geometric Features ◽  
Multi Level

Preoperative assessment of focal liver lesions: multidetector computed tomography challenges magnetic resonance imaging

2005 ◽  
Vol 46 (1) ◽  
pp. 9-15 ◽  
Author(s):  
K. Numminen ◽  
H. Isoniemi ◽  
J. Halavaara ◽  
P. Tervahartiala ◽  
H. Mäkisalo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Magnetic Resonance ◽  
Multidetector Computed Tomography ◽  
Intraoperative Ultrasound ◽  
Preoperative Assessment ◽  
Liver Lesion ◽  
Lesion Detection ◽  
Extrahepatic Disease ◽  
Focal Liver Lesions ◽  
Liver Lesions

Purpose: To investigate prospectively multidetector computed tomography (CT) (MDCT) and magnetic resonance (MR) imaging (MRI) in the preoperative assessment of focal liver lesions. Material and Methods: Multiphasic MDCT and conventional gadolinium‐enhanced MRI were performed on 31 consecutive patients prior to hepatic surgery. All images were blindly analyzed as consensus reading. Lesion counts and their relation to vascular structures and possible extrahepatic disease were determined. The data from the MDCT and MRI were compared with the results obtained by intraoperative ultrasound (IOUS) and palpation. Histopathologic verification was available. Results: At surgery, IOUS and palpation revealed 45 solid liver lesions. From these, preoperative MDCT detected 43 (96%) and MRI 35 (78%) deposits. MDCT performed statistically better than MRI in lesion detection ( P = 0.008). Assessment of lesion vascular proximity was correctly determined by MDCT in 98% of patients and by MRI in 87%. Statistical difference was found ( P = 0.002). IOUS and palpation changed the preoperative surgical plan as a result of extrahepatic disease in 8/31 (26%) cases. In MDCT as well in MRI extrahepatic involvement was suspected in two cases. Conclusion: MDCT was superior to MRI and nearly equal to IOUS in liver lesion detection and in the determination of lesion vascular proximity. However, both techniques fail to reliably detect extrahepatic disease.

Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in CT examinations

2018 ◽  
Vol 275 ◽  
pp. 1585-1594 ◽  
Author(s):  
Avi Ben-Cohen ◽  
Eyal Klang ◽  
Ariel Kerpel ◽  
Eli Konen ◽  
Michal Marianne Amitai ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Liver Lesion ◽  
Lesion Detection ◽  
Convolutional Network ◽  
Fully Convolutional Network

scholarly journals 3D IFPN: Improved Feature Pyramid Network for Automatic Segmentation of Gastric Tumor

2021 ◽  
Vol 11 ◽  
Author(s):  
Haimei Li ◽  
Bing Liu ◽  
Yongtao Zhang ◽  
Chao Fu ◽  
Xiaowei Han ◽  
...  
Keyword(s):  
Liver Tumor ◽  
Feature Fusion ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Learning Ability ◽  
Gastric Tumor ◽  
Tumor Segmentation ◽  
Gastric Tumors ◽  
Multi Level ◽  
Liver Tumor Segmentation

Automatic segmentation of gastric tumor not only provides image-guided clinical diagnosis but also assists radiologists to read images and improve the diagnostic accuracy. However, due to the inhomogeneous intensity distribution of gastric tumors in CT scans, the ambiguous/missing boundaries, and the highly variable shapes of gastric tumors, it is quite challenging to develop an automatic solution. This study designs a novel 3D improved feature pyramidal network (3D IFPN) to automatically segment gastric tumors in computed tomography (CT) images. To meet the challenges of this extremely difficult task, the proposed 3D IFPN makes full use of the complementary information within the low and high layers of deep convolutional neural networks, which is equipped with three types of feature enhancement modules: 3D adaptive spatial feature fusion (ASFF) module, single-level feature refinement (SLFR) module, and multi-level feature refinement (MLFR) module. The 3D ASFF module adaptively suppresses the feature inconsistency in different levels and hence obtains the multi-level features with high feature invariance. Then, the SLFR module combines the adaptive features and previous multi-level features at each level to generate the multi-level refined features by skip connection and attention mechanism. The MLFR module adaptively recalibrates the channel-wise and spatial-wise responses by adding the attention operation, which improves the prediction capability of the network. Furthermore, a stage-wise deep supervision (SDS) mechanism and a hybrid loss function are also embedded to enhance the feature learning ability of the network. CT volumes dataset collected in three Chinese medical centers was used to evaluate the segmentation performance of the proposed 3D IFPN model. Experimental results indicate that our method outperforms state-of-the-art segmentation networks in gastric tumor segmentation. Moreover, to explore the generalization for other segmentation tasks, we also extend the proposed network to liver tumor segmentation in CT images of the MICCAI 2017 Liver Tumor Segmentation Challenge.

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