Performance of a fully automatic lesion detection system for breast DCE-MRI

PURPOSE: To describe and test a new fully automatic lesion detection system for breast DCE-MRI. MATERIALS AND METHODS: Studies were collected from two institutions adopting different DCE-MRI sequences, one with and the other one without fat-saturation. The detection pipeline consists of (i) breast segmentation, to identify breast size and location; (ii) registration, to correct for patient movements; (iii) lesion detection, to extract contrast-enhanced regions using a new normalization technique based on the contrast-uptake of mammary vessels; (iv) false positive (FP) reduction, to exclude contrast-enhanced regions other than lesions. Detection rate (number of system-detected malignant and benign lesions over the total number of lesions) and sensitivity (system-detected malignant lesions over the total number of malignant lesions) were assessed. The number of FPs was also assessed. RESULTS: Forty-eight studies with 12 benign and 53 malignant lesions were evaluated. Median lesion diameter was 6 mm (range, 5-15 mm) for benign and 26 mm (range, 5-75 mm) for malignant lesions. Detection rate was 58/65 (89%; 95% confidence interval [CI] 79%-95%) and sensitivity was 52/53 (98%; 95% CI 90%-99%). Mammary median FPs per breast was 4 (1st-3rd quartiles 3-7.25). CONCLUSION: The system showed promising results on MR datasets obtained from different scanners producing fat-sat or non-fat-sat images with variable temporal and spatial resolution and could potentially be used for early diagnosis and staging of breast cancer to reduce reading time and to improve lesion detection. Further evaluation is needed before it may be used in clinical practice

A Pipelined Tracer-Aware Approach for Lesion Segmentation in Breast DCE-MRI

The recent spread of Deep Learning (DL) in medical imaging is pushing researchers to explore its suitability for lesion segmentation in Dynamic Contrast-Enhanced Magnetic-Resonance Imaging (DCE-MRI), a complementary imaging procedure increasingly used in breast-cancer analysis. Despite some promising proposed solutions, we argue that a “naive” use of DL may have limited effectiveness as the presence of a contrast agent results in the acquisition of multimodal 4D images requiring thorough processing before training a DL model. We thus propose a pipelined approach where each stage is intended to deal with or to leverage a peculiar characteristic of breast DCE-MRI data: the use of a breast-masking pre-processing to remove non-breast tissues; the use of Three-Time-Points (3TP) slices to effectively highlight contrast agent time course; the application of a motion-correction technique to deal with patient involuntary movements; the leverage of a modified U-Net architecture tailored on the problem; and the introduction of a new “Eras/Epochs” training strategy to handle the unbalanced dataset while performing a strong data augmentation. We compared our pipelined solution against some literature works. The results show that our approach outperforms the competitors by a large margin (+9.13% over our previous solution) while also showing a higher generalization ability

Advancing breast cancer screening through the integration of artificial intelligence and ultrafast MRI

This thesis is dedicated to advancing breast cancer screening and diagnosis through the development of artificial intelligence (AI) models for ultrafast breast MRI analysis. Breast cancer, a global health concern, underscores the need for early detection. While mammography is widely used, its limitations necessitate improved screening methods. Dynamic contrast-enhanced MRI, known for high sensitivity, holds promise, particularly for women with dense breasts. This thesis presents a comprehensive approach with AI models that collectively address various aspects of breast cancer screening, from identifying normal scans to locating lesions, distinguishing benign from malignant cases, and improving density assessment. These models offer the potential to enhance screening efficiency, accessibility, and personalization, ultimately improving early breast cancer detection and patient care

Breast Cancer MRI Classification Based on Fractional Entropy Image Enhancement and Deep Feature Extraction

سرطان الثدي يعتبر واحد من الامراض القاتلة الشائعة بين النساء في جميع أنحاء العالم. والتشخيص المبكر لسرطان الثدي الكشف المبكر من أهم استراتيجيات الوقاية الثانوية. نظرًا لاستخدام التصوير الطبي على نطاق واسع في تشخيص العديد من الأمراض المزمنة ومراقبتها، فقد تم اقتراح العديد من خوارزميات معالجة الصور على مر السنين لزيادة مجال التصوير الطبي بحيث تصبح عملية التشخيص أكثر دقة وكفاءة. تقدم هذه الدراسة خوارزمية جديدة لاستخراج الخواص العميقة من نوعين من صور الرنين المغناطيسي T2W-TSE و STIR MRI كمدخلات للشبكات العصبية العميقة المقترحة والتي تُستخدم لاستخراج الخواص للتمييز بين فحوصات التصوير بالرنين المغناطيسي للثدي المرضية والصحية. في هذه الخوارزمية، تتم معالجة فحوصات التصوير بالرنين المغناطيسي للثدي مسبقًا قبل خطوة استخراج الخواص لتقليل تأثيرات الاختلافات بين شرائح التصوير بالرنين المغناطيسي، وفصل الثدي الايمن عن الايسر، بالإضافة الى عزل خلفية الصور. وقد كانت أقصى دقة تم تحقيقها لتصنيف مجموعة بيانات تضم 326 شريحة تصوير بالرنين المغناطيسي للثدي 98.77٪. يبدو أن النموذج يتسم بالكفاءة والأداء ويمكن بالتالي اعتباره مرشحًا للتطبيق في بيئة سريرية.Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature extraction step to enhance and preserve the fine details of the breast MRI scans boundaries by using fractional integral entropy FIE algorithm, to reduce the effects of the intensity variations between MRI slices, and finally to separate the right and left breast regions by exploiting the symmetry information. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, all extracted features significantly improves the performance of the LSTM network to precisely discriminate between pathological and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 326 T2W-TSE images and 326 STIR images is 98.77%. The experimental results demonstrate that FIE enhancement method improve the performance of CNN in classifying breast MRI scans. The proposed model appears to be efficient and might represent a useful diagnostic tool in the evaluation of MRI breast scans

Multi-Modality Breast MRI Segmentation Using nn-UNet for Preoperative Planning of Robotic Surgery Navigation

Segmentation of the chest region and breast tissues is essential for surgery planning and navigation. This paper proposes the foundation for preoperative segmentation based on two cascaded architectures of deep neural networks (DNN) based on the state-of-the-art nnU-Net. Additionally, this study introduces a polyvinyl alcohol cryogel (PVA-C) breast phantom based on the segmentation of the DNN automated approach, enabling the experiments of navigation system for robotic breast surgery. Multi-modality breast MRI datasets of T2W and STIR images were acquired from 10 patients. Segmentation evaluation utilized the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. First, a single class labeling was used to segment the breast region. Then it was employed as an input for three-class labeling to segment fat, fibroglandular (FGT) tissues, and tumorous lesions. The first architecture has a 0.95 DCS, while the second has a 0.95, 0.83, and 0.41 for fat, FGT, and tumor classes, respectively

Pattern classification approaches for breast cancer identification via MRI: state‐of‐the‐art and vision for the future

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI) of breast tissue are discussed. The algorithms are based on recent advances in multidimensional signal processing and aim to advance current state‐of‐the‐art computer‐aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi‐parametric computer‐aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi‐supervised deep learning and self‐supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high‐dimensional medical imaging analysis platform that is based on multi‐task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE‐MRI. Since some of the approaches discussed are also based on time‐lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis

Breast Cancer Analysis in DCE-MRI

Breast cancer is the most common women tumour worldwide, about 2 million new cases diagnosed each year (second most common cancer overall). This disease represents about 12% of all new cancer cases and 25% of all cancers in women. Early detection of breast cancer is one of the key factors in determining the prognosis for women with malignant tumours. The standard diagnostic tool for the detection of breast cancer is x-ray mammography. The disadvantage of this method is its low specificity, especially in the case of radiographically dense breast tissue (young or under-forty women), or in the presence of scars and implants within the breast. Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has demonstrated a great potential in the screening of high-risk women for breast cancer, in staging newly diagnosed patients and in assessing therapy effects. However, due to the large amount of information, DCE-MRI manual examination is error prone and can hardly be inspected without the use of a Computer-Aided Detection and Diagnosis (CAD) system. Breast imaging analysis is made harder by the dynamical characteristics of soft tissues since any patient movements (such as involuntary due to breathing) may affect the voxel-by-voxel dynamical analysis. Breast DCE-MRI computer-aided analysis needs a pre-processing stage to identify breast parenchyma and reduce motion artefacts. Among the major issues in developing CAD for breast DCE-MRI, there is the detection and classification of lesions according to their aggressiveness. Moreover, it would be convenient to determine those subjects who are likely to not respond to the treatment so that a modification may be applied as soon as possible, relieving them from potentially unnecessary or toxic treatments. In this thesis, an automated CAD system is presented. The proposed CAD aims to support radiologist in lesion detection, diagnosis and therapy assessment after a suitable preprocessing stage. Segmentation of breast parenchyma has been addressed relying on fuzzy binary clustering, breast anatomical priors and morphological refinements. The breast mask extraction module combines three 2D Fuzzy C-Means clustering (executed from the three projection, axial, coronal and transversal) and geometrical breast anatomy characterization. In particular, seven well-defined key-points have been considered in order to accurately segment breast parenchyma from air and chest-wall. To diminish the effects of involuntary movement artefacts, it is usual to apply a motion correction of the DCE-MRI volumes before of any data analysis. However, there is no evidence that a single Motion Correction Technique (MCT) can handle different deformations - small or large, rigid or non-rigid - and different patients or tissues. Therefore, it would be useful to develop a quality index (QI) to evaluate the performance of different MCTs. The existent QI might not be adequate to deal with DCE-MRI data because of the intensity variation due to contrast media. Therefore, in developing a novel QI, the underlying idea is that once DCE-MRI data have been realigned using a specific MCT, the dynamic course of the signal intensity should be as close as possible to physiological models, such as the currently accepted ones (e.g. Tofts-Kermode, Extended Tofts-Kermode, Hayton-Brady, Gamma Capillary Transit Time, etc.). The motion correction module ranks all the MCTs, using the QI, selects the best MCT and applies a correction before of further data analysis. The proposed lesion detection module performs the segmentation of lesions in Regions of Interest (ROIs) by means of classification at a pixel level. It is based on a Support Vector Machine (SVM) trained with dynamic features, extracted from a suitably pre-selected area by using a pixel-based approach. The pre-selection mask strongly improves the final result. The lesion classification module evaluates the malignity of each ROI by means of 3D textural features. The Local Binary Patterns descriptor has been used in the Three Orthogonal Planes (LBP-TOP) configuration. A Random Forest has been used to achieve the final classification into a benignant or malignant lesion. The therapy assessment stage aims to predict the patient primary tumour recurrence to support the physician in the evaluation of the therapy effects and benefits. For each patient which has at least a malignant lesion, the recurrence of the disease has been evaluated by means of a multiple classifiers system. A set of dynamic, textural, clinicopathologic and pharmacokinetic features have been used to assess the probability of recurrence for the lesions. Finally, to improve the usability of the proposed work, we developed a framework for tele-medicine that allows advanced medical image remote analysis in a secure and versatile client-server environment, at a low cost. The benefits of using the proposed framework will be presented in a real-case scenario where OsiriX, a wide-spread medical image analysis software, is allowed to perform advanced remote image processing in a simple manner over a secure channel. The proposed CAD system have been tested on real breast DCE-MRI data for the available protocols. The breast mask extraction stage shows a median segmentation accuracy and Dice similarity index of 98% (+/-0,49) and 93% %(+/-1,48) respectively and 100% of neoplastic lesion coverage. The motion correction module is able to rank the MCTs with an accordance of 74% with a 'reference ranking'. Moreover, by only using 40% of the available volume, the computational load is reduced selecting always the best MCT. The automatic detection maximises the area of correctly detected lesions while minimising the number of false alarms with an accuracy of 99% and the lesions are, then, diagnosed according to their stage with an accuracy of 85%. The therapy assessment module provides a forecasting of the tumour recurrence with an accuracy of 78% and an AUC of 79%. Each module has been evaluated by a leave-one-patient-out approach, and results show a confidence level of 95% (p<0.05). Finally, the proposed remote architecture showed a very low transmission overhead which settles on about 2.5% for the widespread 10\100 Mbps. Security has been achieved using client-server certificates and up-to-date standards

A computerized volumetric segmentation method applicable to multi-centre MRI data to support computer-aided breast tissue analysis, density assessment and lesion localization.

Density assessment and lesion localization in breast MRI require accurate segmentation of breast tissues. A fast, computerized algorithm for volumetric breast segmentation, suitable for multi-centre data, has been developed, employing 3D bias-corrected fuzzy c-means clustering and morphological operations. The full breast extent is determined on T1-weighted images without prior information concerning breast anatomy. Left and right breasts are identified separately using automatic detection of the midsternum. Statistical analysis of breast volumes from eighty-two women scanned in a UK multi-centre study of MRI screening shows that the segmentation algorithm performs well when compared with manually corrected segmentation, with high relative overlap (RO), high true-positive volume fraction (TPVF) and low false-positive volume fraction (FPVF), and has an overall performance of RO 0.94 ± 0.05, TPVF 0.97 ± 0.03 and FPVF 0.04 ± 0.06, respectively (training: 0.93 ± 0.05, 0.97 ± 0.03 and 0.04 ± 0.06; test: 0.94 ± 0.05, 0.98 ± 0.02 and 0.05 ± 0.07)