Automatic lesion detection, segmentation and characterization via 3D
multiscale morphological sifting in breast MRI
- URL: http://arxiv.org/abs/2007.03199v1
- Date: Tue, 7 Jul 2020 04:39:13 GMT
- Title: Automatic lesion detection, segmentation and characterization via 3D
multiscale morphological sifting in breast MRI
- Authors: Hang Min, Darryl McClymont, Shekhar S. Chandra, Stuart Crozier and
Andrew P. Bradley
- Abstract summary: 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.
Compared with previously proposed systems evaluated on the same breast MRI dataset, the proposed CAD system achieves a favourable performance in breast lesion detection and characterization.
- Score: 3.4400216692203998
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Previous studies on computer aided detection/diagnosis (CAD) in 4D breast
magnetic resonance imaging (MRI) 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 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 for lesion segmentation. Compared with previously proposed
systems evaluated on the same breast MRI dataset, the proposed CAD system
achieves a favourable performance in breast lesion detection and
characterization.
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