A Novel Computer Aided Detection (CADe) Scheme for Colonic Polyps Based on the Structure Decomposition

Wang, Huafeng; Zhang, Liang; Li, Lihong; Peng, Hao; Han, Hongbin; Liu, Yan; Han, Fangfang; Gu, Xianfeng

doi:10.1007/978-3-642-41083-3_8

Cited by 4 publications

(2 citation statements)

References 11 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this preliminary work, our purpose is to show the improved detection power by using the colon wall thickness as an effective feature while false positive reduction is not emphasized. In our future work, the number of false detections may be reduced by using colon structure decomposition 24 , where colon can be decomposed into haustral folds and normal wall and, therefore, all haustral folds can be eliminated from the pool of IPCs.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Detection of colonic polyp candidates with level set-based thickness mapping over the colon wall

Han

Duan

et al. 2015

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

Further improvement of computer-aided detection (CADe) of colonic polyps is vital to advance computed tomographic colonography (CTC) toward a screening modality, where the detection of flat polyps is especially challenging because limited image features can be extracted from flat polyps, and the traditional geometric features-based CADe methods usually fail to detect such polyps. In this paper, we present a novel pipeline to automatically detect initial polyp candidates (IPCs), especially flat polyps, from CTC images. First, the colon wall mucosa was extracted via a partial volume segmentation approach as a volumetric layer, where the inner border of colon wall can be obtained by shrinking the volumetric layer using level set based adaptive convolution. Then the outer border of colon wall (or the colon wall serosa) was segmented via a combined implementation of geodesic active contour and Mumford-Shah functional in a coarse-to-fine manner. Finally, the wall thickness was estimated along a unique path between the segmented inner and outer borders with consideration of the volumetric layers and was mapped onto a patient-specific three-dimensional (3D) colon wall model. The IPC detection results can usually be better visualized in a 2D image flattened from the 3D model, where abnormalities were detected by Z-score transformation of the thickness values. The proposed IPC detection approach was validated on 11 patients with 22 CTC scans, and each scan has at least one flat poly annotation. The above presented novel pipeline was effective to detect some flat polyps that were missed by our CADe system while keeping false detections in a relative low level. This preliminary study indicates that the presented pipeline can be incorporated into an existing CADe system to enhance the polyp detection power, especially for flat polyps.

show abstract

Section: Conclusion and Discussionmentioning

confidence: 99%

Detection of colonic polyp candidates with level set-based thickness mapping over the colon wall

Han

Duan

et al. 2015

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mang et al [21] evaluated the stand-alone performance of a polyp detection method with and without fecal tagging, and showed no significant performance differences. Wang et al [32] provided a novel polyp detection scheme based on decomposition of complex colon into uniform broken parts using second order derivatives of the volumetric image.…”

Section: Introductionmentioning

confidence: 99%

Adaptive kernel based multiple kernel learning for computer-aided polyp detection in CT colonography

Han

et al. 2015

Geometry, Imaging and Computing

Self Cite

View full text Add to dashboard Cite

Computer-aided detection (CAD) of colonic polyps, as a second reader for computed tomographic colonography (CTC) screening, has earned extensive research interest over the past decades. False positive (FP) reduction in the CAD system plays a crucial role in detecting the polyps. To improve the performance of FP reduction and better assist the physician's diagnosis, we propose an adaptive kernel based multiple kernel learning (MKL) method for CAD of colonic polyps, called AK-MKL. This method builds a more adaptive synthesized classifier by incorporating an adaptive kernel into a set of predefined base kernels for better performance in differentiating true polyps from FPs, which is implemented by learning an optimal combination of a collection of those kernel-based classifiers. Performance evaluation for the presented AK-MKL method was performed on a CTC database, consisting of 25 patients with 50 CT scans. In terms of the AUC (area under the curve of receiver operating characteristic) and accuracy merits, the experimental results showed that our AK-MKL method achieves better performance, compared with two other different methods, i.e., one classifier based on support vector machine (SVM) with only one adaptive kernel (AK-SVM) and the other one based on multiple kernel learning only (MKL).

show abstract

Computer-aided detection for CT colonography

Zhao

2014

J. Shanghai Jiaotong Univ. (Sci.)

View full text Add to dashboard Cite

Large Language Models (LLMs) are composed of neurons that exhibit various behaviors and roles, which become increasingly diversified as models scale. Recent studies have revealed that not all neurons are active across different datasets, and this sparsity correlates positively with the task-specific ability, leading to advancements in model pruning and training efficiency. Traditional fine-tuning methods engage all parameters of LLMs, which is computationally expensive and may not be necessary. In contrast, Parameter-Efficient Fine-Tuning (PEFT) approaches aim to minimize the number of trainable parameters, yet they still operate at a relatively macro scale (e.g., layer-level). We introduce Neuron-Level Fine-Tuning (NeFT), a novel approach that refines the granularity of parameter training down to the individual neuron, enabling more precise and computationally efficient model updates. The experimental results show that NeFT not only exceeded the performance of full-parameter fine-tuning and PEFT but also provided insights into the analysis of neurons.

show abstract

A Novel Computer Aided Detection (CADe) Scheme for Colonic Polyps Based on the Structure Decomposition

Cited by 4 publications

References 11 publications

Detection of colonic polyp candidates with level set-based thickness mapping over the colon wall

Detection of colonic polyp candidates with level set-based thickness mapping over the colon wall

Adaptive kernel based multiple kernel learning for computer-aided polyp detection in CT colonography

Computer-aided detection for CT colonography

Contact Info

Product

Resources

About