Computer assisted detection and characterisation of breast cancer in MRI

McClymont, Darryl

doi:10.14264/uql.2015.459

Cited by 3 publications

(23 citation statements)

References 161 publications

(266 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each lesion was individually biopsied under either MRI or ultrasound guidance. The detail of lesion pathology can be found in [92,111]. We use the T1-w anatomical, T2-w anatomical and the DCE-MRI data in the experiments.…”

Section: Breast Mri Databasementioning

confidence: 99%

“…Mean area under receiver operating characteristics (ROC) curve (AUC) [54] 1. Mean in VOI relative to fat [60,92] 2. Standard deviation in VOI relative to fat [60,92] 3.…”

Section: Mean Of Maximum Laplacian In Transverse Directionmentioning

confidence: 99%

“…Mean in VOI relative to fat [60,92] 2. Standard deviation in VOI relative to fat [60,92] 3. 20 th percentile in VOI [93] 4.…”

Section: Mean Of Maximum Laplacian In Transverse Directionmentioning

confidence: 99%

“…Radial gradient index [11] 11. Presence of oedema (98 th percentile in 20mm shell) [92,93] 12. Mean of eroded VOI [92,93] over 10-fold CV is used as a performance measure.…”

Section: Mean Of Maximum Laplacian In Transverse Directionmentioning

confidence: 99%

“…Presence of oedema (98 th percentile in 20mm shell) [92,93] 12. Mean of eroded VOI [92,93] over 10-fold CV is used as a performance measure. For both CADx and CADe studies, we perform a t-test to evaluate the significance of the difference between the AUC obtained with CkNN and single instance kNN and RF.…”

Section: Mean Of Maximum Laplacian In Transverse Directionmentioning

confidence: 99%

See 4 more Smart Citations

Multiple Instance Learning for Breast Cancer Magnetic Resonance Imaging

Maken

Gal

McClymont

et al. 2014

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

Self Cite

View full text Add to dashboard Cite

In this thesis we evaluate the efficacy of multiple instance learning (MIL) as a 'pure' machine learning approach for the diagnosis of breast cancer in magnetic resonance images (MRI). The traditional approach for the diagnosis of breast cancer is based on region-of-interest (ROI) based single instance learning (SIL). In the ROI-based SIL, the classification of benign and malignant lesions depends on the features, which are extracted from segmented ROIs. But, an accurate segmentation of a ROI is a challenging task due to poor signal-to-noise-ratio and faint edges due to partial volume effects. Therefore, variations in the segmentation of ROIs can affect the diagnostic outcome. MIL is a relatively new method in the supervised learning, where each sample is represented as a bag of instances. An image in the context of MIL can be considered as a bag of pixels, tiles, or ROIs, which correspond to instances. Here we apply tile-based MIL, where the diagnosis of breast cancer is based on tile-based features which do not require the segmentation of ROIs. Therefore, tile-based MIL has the potential to provide the classification of breast cancer without segmenting the ROIs. This is the motivation for the main objective of this thesis: to estimate the efficacy of tile-based MIL in the detection and the diagnosis of breast cancer MRI.We initially evaluate the potential of MIL for the detection and the diagnosis of breast cancer in anatomical T2-weighted MRI. In particular, we compare the performance of a MIL-based learner, i.e., citation-kNN (CkNN) against conventional kNN and a Random Forest classifier. We utilise both (generic) tile-based spatial features and (domain specific) ROI-based features. We perform experiments on two datasets consisting of 77 mass-like lesions and 129 both mass-like and non-mass-like lesions. The performance of CkNN as both a diagnostic and screening tool is evaluated using the area under the receiver operating characteristic curve (AUC), estimated over 10-fold cross validation. Results demonstrate that the tile-based CkNN has equivalent performance to the ROI-based classification. However, tile-based MIL has an advantage that it does not require the domain specific ROI-based features typically used in breast MRI. This not only has the potential to make the tile-based classification robust to inaccuracies in the delineation of suspicious lesions, but also makes it suitable for the detection of suspicious lesions prior to segmentation.Next, we investigate the performance of CkNN for the diagnosis of breast cancer using dynamic MRI. Specifically we use generic tile-based spatio-temporal features derived from T2-weighted MRI and T1-weighted dynamic contrast enhanced MRI. We utilise a discrete cosine transform and contrast enhancement models as feature extraction techniques. We compare the III performance of CkNN and kNN against a traditional approach based on bespoke ROI-based features using the 77 mass-like lesions. Empirical results show the equivalent classification performance of both ti...

show abstract

Section: Breast Mri Databasementioning

confidence: 99%

“…Mean area under receiver operating characteristics (ROC) curve (AUC) [54] 1. Mean in VOI relative to fat [60,92] 2. Standard deviation in VOI relative to fat [60,92] 3.…”

Section: Mean Of Maximum Laplacian In Transverse Directionmentioning

confidence: 99%

“…Mean in VOI relative to fat [60,92] 2. Standard deviation in VOI relative to fat [60,92] 3. 20 th percentile in VOI [93] 4.…”

Section: Mean Of Maximum Laplacian In Transverse Directionmentioning

confidence: 99%

“…Radial gradient index [11] 11. Presence of oedema (98 th percentile in 20mm shell) [92,93] 12. Mean of eroded VOI [92,93] over 10-fold CV is used as a performance measure.…”

Section: Mean Of Maximum Laplacian In Transverse Directionmentioning

confidence: 99%

Section: Mean Of Maximum Laplacian In Transverse Directionmentioning

confidence: 99%

See 3 more Smart Citations

Multiple Instance Learning for Breast Cancer Magnetic Resonance Imaging

Maken

Gal

McClymont

et al. 2014

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

Self Cite

View full text Add to dashboard Cite

show abstract

Pre and post-hoc diagnosis and interpretation of malignancy from breast DCE-MRI

Maicas

Bradley

Nascimento

et al. 2019

Medical Image Analysis

View full text Add to dashboard Cite

We propose a new method for breast cancer screening from DCE-MRI based on a post-hoc approach that is trained using weakly annotated data (i.e., labels are available only at the image level without any lesion delineation). Our proposed post-hoc method automatically diagnosis the whole volume and, for positive cases, it localizes the malignant lesions that led to such diagnosis. Conversely, traditional approaches follow a pre-hoc approach that initially localises suspicious areas that are subsequently classified to establish the breast malignancy -this approach is trained using strongly annotated data (i.e., it needs a delineation and classification of all lesions in an image). Another goal of this paper is to establish the advantages and disadvantages of both approaches when applied to breast screening from DCE-MRI. Relying on experiments on a breast DCE-MRI dataset that contains scans of 117 patients, our results show that the post-hoc method is more accurate for diagnosing the whole volume per patient, achieving an AUC of 0.91, while the pre-hoc method achieves an AUC of 0.81. However, the performance for localising the malignant lesions remains challenging for the post-hoc method due to the weakly labelled dataset employed during training.

show abstract

Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI

Huang¹,

McClymont²,

Chandra³

et al. 2020

Biomed. Phys. Eng. Express

Self Cite

View full text Add to dashboard Cite

Previous studies on computer aided detection/diagnosis (CAD) in 4D breast magnetic resonance imaging (MRI) usually regard lesion detection, segmentation and characterization as separate tasks, and typically require users to manually select 2D MRI slices or regions of interest as the input. In this work, we present a breast MRI CAD system that can handle 4D multimodal breast MRI data, and integrate lesion detection, segmentation and characterization with no user intervention. The proposed CAD system consists of three major stages: region candidate generation, feature extraction and region candidate classification. Breast lesions are firstly extracted as region candidates using the novel 3D multiscale morphological sifting (MMS). The 3D MMS, which uses linear structuring elements to extract lesion-like patterns, can segment lesions from breast images accurately and efficiently. Analytical features are then extracted from all available 4D multimodal breast MRI sequences, including T1-, T2-weighted and DCE sequences, to represent the signal intensity, texture, morphological and enhancement kinetic characteristics of the region candidates. The region candidates are lastly classified as lesion or normal tissue by the random under-sampling boost (RUSboost), and as malignant or benign lesion by the random forest. Evaluated on a breast MRI dataset which contains a total of 117 cases with 141 biopsy-proven lesions (95 malignant and 46 benign lesions), the proposed system achieves a true positive rate (TPR) of 0.90 at 3.19 false positives per patient (FPP) for lesion detection and a TPR of 0.91 at a FPP of 2.95 for identifying malignant lesions without any user intervention. The average dice similarity index (DSI) is 0.72 ± 0.15 for lesion segmentation. Compared with previously proposed lesion detection, detection-segmentation and detection-characterization systems evaluated on the same breast MRI dataset, the proposed CAD system achieves a favourable performance in breast lesion detection and characterization.

show abstract

Computer assisted detection and characterisation of breast cancer in MRI

Cited by 3 publications

References 161 publications

Multiple Instance Learning for Breast Cancer Magnetic Resonance Imaging

Multiple Instance Learning for Breast Cancer Magnetic Resonance Imaging

Pre and post-hoc diagnosis and interpretation of malignancy from breast DCE-MRI

Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI

Contact Info

Product

Resources

About