Data-driven techniques based on Artificial Neural Networks (ANN) provide a basis for a more explorative approach to DCE-MRI data analysis. Unsupervised ANNs can directly be applied for the analysis of the DCE-MRI data recorded for the case which is currently under investigation. Thereby, a predefined number of clusters of signals is determined. Each cluster consists of temporal kinetic signals which are similar in the signal space according to a certain metric and may refer to a specific type of tissue such as benign or malignant. The outcome of the clustering process can be displayed as a three-dimensional image in which the colour of each voxel reflects the index of the prototype which is most similar to the corresponding temporal kinetic signal.
While unsupervised ANNs autonomously identify reasonable signal clusters from the data, su- pervised ANNs are applied in order to distinguish predefined signal classes, e.g. signals of normal and suspicious tissue. To this end, supervised ANNs correlate the signal information of the DCE- MR images with label information such as manual segmentations of lesions during an adaptation or training process. After adaptation of the ANN with the labelled data of a small cohort of cases, the trained ANN can be applied to infer the specified label, which is unknown for unseen cases, from the corresponding DCE-MRI sequences.
In contrast to model-based techniques which are limited to analysing temporal kinetic patterns, ANNs facilitate simultaneous processing of more general input patterns which may combine mor- phological features, features of the temporal dynamics of signals, multiparameter information derived from images with different T1/T2 weighting or texture information computed for small
image patches. On the other hand, the outcome of the data-driven techniques typically can not be interpreted in terms of physiologically meaningful quantities.
3.6.1 Applications of Supervised Artificial Neural Networks
Supervised ANNs, primarily multilayer perceptrons (MLP), have been employed for the evaluation of DCE-MRI data in different setups.
Lucht et al., 2001 employed a MLP to classify temporal kinetic signals of carcinoma, fibroade- noma, benign proliferative changes and parenchyma. Training data was collected by measuring average kinetic patterns of ROIs which were placed on compartments of lesions according to the parametric map obtained from a pharmacokinetic model. The trained classifier revealed 84% sensitivity and 81% specificity for the discrimination of benign and malignant signals but a poor performance for the subclassification of benign signals in fibroadenoma or benign prolifera- tive changes. Additional experiments indicated an optimal performance for temporal sequences consisting of 28 measurements and a clearly reduced performance for sequences consisting of 3 measurements (78% sensitivity and 76% specificity). In [Lucht et al., 2002] the same setup was used for supervised segmentation of entire image volumes.
Tzacheva et al., 2003 utilised a MLP for classifying entire lesions as benign or malignant. The input patterns were features derived from a single static contrast-enhanced magnetic reso- nance image (CE-MRI) which was recorded with an imaging protocol with active fat suppression. Strong enhancing regions, i.e. potentially cancerous masses, were identified by a region-oriented segmentation of the contrast-enhanced image based on intensity thresholds. The segmented im- age was subsequently converted into a binary image. For each positive region, a pattern vector was computed combining mass margin and mass shape features in addition to simple texture features. These feature vectors were evaluated by a MLP to distinguish malignant regions from parenchyma and blood vessels. The system yielded 90% sensitivity and 91% specificity.
Abdolmaleki et al., 2001 trained a MLP to distinguish averaged kinetic signals of ROIs which were manually placed by a radiologist over malignant or benign tissue. The input pattern associ- ated with each ROI consisted of quantitative features extracted from the kinetic signal such as the area-under-the-signal-curve, steepest slope in the wash-in part or the signal intensity after one, two and five minutes. Two additional features represented the age of the patient and the size of the associated ROI. A comparison of the ANN classification performance with those from a group of experienced radiologists and a group of low-experienced radiologists indicated that the ANN (97% sensitivity and 64% specificity) outperforms the latter and yields a performance comparable with an experienced radiologist.
The mentioned applications of supervised algorithms have in common that they either depend on dedicated imaging protocols [Tzacheva et al., 2003] or an extensive interaction with the user. The setup proposed by Lucht et al., 2002 requires the radiologist to manually place ROIs in the DCE-MR images in order to collect examples of tumour signals subsequently used for MLP training. A model-based technique (a pharmacokinetic model) is utilised for guiding the ROI placement, which in general is undesirable for the development of a second data-driven analysis setup. Abdolmaleki et al., 2001 propose a ANN based evaluation of average kinetic signals of ROIs, which were placed over the most enhancing region of each lesion. Since the same working step has to be performed for each new case, the application of this approach still depends on
interactions with the user. Additionally, the preceding working step, i.e. the localisation and delineation of the extent of the suspicious mass itself, is not considered in this work. In fact, results of a detailed comparison of learning algorithm based approaches for the localisation and delineation of suspicious masses with clinical standard procedures such as the manual evaluation of subtraction images have not yet been reported.
Apart from the shortcomings of their conceptional design, all most approaches have in common that they employ a multilayer perceptron. Even though the MLP is perhaps the most frequently used supervised classification algorithm in biomedical applications, recent advances in the area of machine learning and artificial neural networks have led to new supervised learning techniques which are easier to handle and are likely to achieve superior classification performance. For instance kernel-based techniques such as the support vector machine have shown impressive results in various applications. At the same time, they only require a small number of systematically tuneable hyperparameters as described in the following chapter.
3.6.2 Applications of Unsupervised Artificial Neural Networks
Several variants of unsupervised learning techniques have been employed for the purpose of visual- isation and exploration of DCE-MRI data. These techniques solely utilise information as provided by the input patterns and reveal clusters of examples with similar signal characteristics or provide a compact display by transforming the high dimensional data.
Wism¨uller et al., 2002 and Meyer-B¨ase et al., 2004 examined the application of vector quanti- sation (VQ) algorithms for learning of prototypes of temporal kinetic signals representing clusters in the signal space consisting of kinetic signals with similar characteristics. Wism¨uller et al., 2002 subsequently used the prototypes to segment the image volumes. Each voxel was labelled with the index of the prototype which is most similar to the associated temporal kinetic signal. The authors were able to segment the lesion mass from surrounding tissue as well as to find subdivisions of lesions in compartments with homogenous signal characteristic.
Jacobs et al., 2003 applied the iterative self-organising data analysis (ISODATA) algorithm, i.e. a kmeans-like VQ algorithm with a dynamic number of prototypes, for processing multiparameter DCE-MRI data. The input patterns were different combinations of static contrast-enhanced and non-enhanced MR images with a T1- or T2-weighting and optional fat-suppression. Based on the clustering result, a score was derived for the discrimination of malignant and benign lesions. Using the T1- and T2-weighted, enhanced and non-enhanced static images as features, the classification yielded 89% sensitivity and 74% specificity which is comparable to the performance of approaches evaluating the temporal kinetic signals of T1-weighted image sequences.
The determination of typical temporal kinetic signals which are more reliable than signals aver- aged over entire lesions was considered by Chen et al., 2004. A fuzzy c-means (FCM) clustering algorithm was applied for segmenting lesion masses in compartments of similar signals, each rep- resented by an averaged temporal kinetic pattern with lower variance. The averaged temporal kinetic pattern exhibiting the strongest initial enhancement was used as a prototype for the le- sion. For the following classification of the lesion as benign or malignant, the quantitative features maximum uptake, peak location, uptake rate and wash-out rate were calculated and evaluated by a linear discriminant analysis (LDA). The comparison of the classification performance with that yielded by a LDA evaluating the temporal kinetic pattern averaged over the entire lesion indicated
significantly increased performance of the FCM based classification scheme.
Yoo et al., 2002 applied independent component analysis (ICA) for the purpose of lesion detec- tion and characterisation. The extracted independent components were interpreted as reference waveforms showing certain characteristics of the temporal kinetic signals. Subsequently, the DCE- MR sequence was displayed as a single three-dimensional grey value images with voxel intensities reflecting the correlation of the corresponding temporal kinetic pattern with a user-selected ref- erence waveform. Since the ICA was able to extract reference waveforms with typical signal characteristics of malignant tissue, the malignant compartments of the lesions were highlighted in the corresponding correlation images.
3.7 Summary
DCE-MRI has proven to be a valuable imaging technique which is highly sensitive to the vascular changes of cancerous tissues. Beside morphological features, the key-information is the course of the concentration of contrast agent molecules in the tissue as measured in form of temporal se- quences of three-dimensional MR images. Examination of the temporal kinetic signals enables the investigator to localise and characterise suspicious masses, but is a laborious and time-consuming task due to the multitemporal nature and the large amount of image data.
Computer aided diagnosis systems using model-based or data-driven techniques are currently under investigation. Model-based approaches are based on explicitly defined mathematical models of underlying physiological processes and demand for a-priori knowledge. Even though the phar- macokinetical models use several convenient simplifications, they provide valuable information with a clear histopathological meaning.
Data-driven approaches using supervised and unsupervised artificial neural networks have re- cently been identified as an appropriate alternative technique. ANN based systems derive implicit models of the temporal kinetic signals during data-driven adaptation processes. Thus, they do not rely on a-priori knowledge about the underlying processes and can be applied even if signal characteristics are complex.
In this thesis, modern machine learning techniques and ANNs are applied for the three main work tasks of DCE-MRI analysis, namely efficient data visualisation, localisation of suspicious masses and characterisation of suspicious masses. The goal is to develop applications based on ANNs which are consistently data-driven and do not depend on preprocessing steps based on model- based techniques. The proposed applications will only require data acquired during standard clinical diagnosis processes, which are geared towards the medical requirements of breast cancer diagnosis and not to the requirements of ANN applications.
4 Supervised Learning - Concepts, Algorithms and
Evaluation
In this chapter, basic ideas of supervised learning algorithms are discussed. After a brief review of two principles of supervised learning, the supervised learning algorithms employed in this thesis are introduced. Techniques for evaluating the performance of classification models are described in the last section.