The knowledge base was initially built with a general set of heuristics comparing the effects of different pulse sequences on different types of tissues, as shown in Table 2. This process is called ``knowledge-engineering'' as we had to decide which knowledge was most useful for the goal of tumor segmentation, followed by the process of implementing such information into a rule-based system. More importantly, the training set used was quite small - seventeen slices over three patients. Yet, the system performed well. A larger training set would most likely allow new and more effective trends and characteristics to be revealed. Thresholds used to handle a certain subset of the training set could be better generalized.
The slices processed had a relatively large thickness of 5mm. Thinner slices which exhibit a reduced partial-volume effect and allow better tissue contrast. While relying on feature space distributions, the system was developed using general tissue characteristics, such as those listed in Table 2, and relative relationships between tissues to avoid dependence upon specific feature-domain values. The particular slices were acquired with the same parameters, but gadolinium-enhancement has been found to be generally very robust in different protocols and thickness [48,39]. Should acquisition parameter dependence become an issue, given a large enough training base across multiple parameters, the knowledge base could automatically adjust to a slice's specific parameters since such information is easily included when processing starts. The patient volumes processed had received various degrees of treatment, including surgery, radiation and chemo-therapy both before and between scans. Yet, despite the changes these treatments can cause, such as demyelinization of white matter, no modifications to the knowledge based system were necessary. Other approaches, like neural networks [49] or any sort of supervised method which is based on a specific set of training examples could have difficulties in dealing with slightly different imaging protocols and the effects of treatment.
As stated in the introduction, no method of quantitating tumor volumes is widely accepted and used in clinical practice [4]. A method by the Eastern Cooperative Oncology group [5] approximates tumor area in the single MR slice with the largest contiguous, well-defined tumor evident. The longest tumor diameter is multiplied by its perpendicular to yield an area. Changes greater than 25% in the area of a tumor over time are used, in conjunction with visual observations, to classify tumor response to treatment into five categories from complete response (no measurable tumor left) to progression. This approach does not address full tumor volume, depends on the exact boundary choices, and the shape of the tumor [2,5]. By itself, the approach can lead to inaccurate growth/shrinkage decisions [6].
The promise of the knowledge-based system as a useful tool is demonstrated by the successful performance of the system on the processed slices. The final KB segmentations compare well with radiologist-labeled ``ground truth'' images. The knowledge-based system also compared well with supervised kNN method, and was able to segment tumor without the need for (multiple) human-based ROI's or post-processing, which make kNN clinically impractical. Further, we looked at removing extra-cranial pixels from kNN tumor segmentations and found that kNN then consistently underestimated the tumor size. Also with the extra-cranial pixels removed kNN makes 2 mistakes in following the trend shown in Figure 12 (a).
Future work includes addressing the problems noted in Section 4 to improve the system's performance. The high number of false positives, which appear to be a matter of tumor boundaries, can be reduced by applying a final threshold in T1-space (the feature image used primarily by radiologists in determining final tumor boundaries). Our primary concern was losing as little ground truth tumor as possible. Expanding the training set to include more patients should expand the generalizability of the knowledge base. The next expected development in this system is to expand the processing range to all slices that intersect the brain cerebrum. Introducing new tumor types, such as lower grade gliomas will also be considered, as will complete labeling of all remaining tissues. Also, newer MRI systems may provide additional features, such as diffusion images or edge strength to estimate tumor boundaries, which can be readily included into the knowledge base. The knowledge-base also allows straightforward expansion as new tools are found effective (perhaps edge detection on the tumor mask).
In conclusion, the knowledge-based system is a multi-spectral
tool that shows promise in effectively segmenting
glioblastoma-multiforme tumors without the need for
human supervision. It has the potential of
being a useful tool for segmenting
tumor for therapy planning, and tracking tumor response.
Lastly, the knowledge-based paradigm
allows easy integration of new domain information and
processing tools into the existing system when
other types of pathology and MR data are considered.